Data-driven simulations for training AI-based segmentation of neutron images

Neutron interferometry uniquely combines neutron imaging and scattering methods to enable characterization of multiple length scales from 1 nm to 10 µm. However, building, operating, and using such neutron imaging instruments poses constraints on the acquisition time and on the number of measured images per sample. Experiment time-constraints yield small quantities of measured images that are insufficient for automating image analyses using supervised artificial intelligence (AI) models. One approach alleviates this problem by supplementing annotated measured images with synthetic images. To this end, we create a data-driven simulation framework that supplements training data beyond typical data-driven augmentations by leveraging statistical intensity models, such as the Johnson family of probability density functions (PDFs). We follow the simulation framework steps for an image segmentation task including Estimate PDFs→ Validate PDFs→ Design Image Masks→ Generate Intensities→ Train AI Model for Segmentation. Our goal is to minimize the manual labor needed to execute the steps and maximize our confidence in simulations and segmentation accuracy. We report results for a set of nine known materials (calibration phantoms) that were imaged using a neutron interferometer acquiring four-dimensional images and segmented by AI models trained with synthetic and measured images and their masks.

A Methodology for the Rapid Qualification of Additively Manufactured Materials Based on Pore Defect Structures

Additive manufacturing (AM) can create net or near-net-shaped components while simultaneously building the material microstructure, therefore closely coupling forming the material and shaping the part in contrast to traditional manufacturing with distinction between the two processes. While there are well-heralded benefits to AM, the widespread adoption of AM in fatigue-limited applications is hindered by defects such as porosity resulting from off-nominal process conditions. The vast number of AM process parameters and conditions make it challenging to capture variability in porosity that drives fatigue design allowables during qualification. Furthermore, geometric features such as overhangs and thin walls influence local heat conductivity and thereby impact local defects and microstructure. Consequently, qualifying AM material within parts in terms of material properties is not always a straightforward task. This article presents an approach for rapid qualification of AM fatigue-limited parts and includes three main aspects: (1) seeding pore defects of specific size, distribution, and morphology into AM specimens, (2) combining non-destructive and destructive techniques for material characterization and mechanical fatigue testing, and (3) conducting microstructure-based simulations of fatigue behavior resulting from specific pore defect and microstructure combinations. The proposed approach enables simulated data to be generated to validate and/or augment experimental fatigue data sets with the intent to reduce the number of tests needed and promote a more rapid route to AM material qualification. Additionally, this work suggests a closer coupling between material qualification and part certification for determining material properties at distinct regions within an AM part.

Enabling Chemoenzymatic Strategies and Enzymes for Synthesizing Sialyl Glycans and Sialyl Glycoconjugates

ConspectusSialic acids are fascinating negatively charged nine-carbon monosaccharides. Sialic acid-containing glycans and glycoconjugates are structurally diverse, functionally important, and synthetically challenging molecules. We have developed highly efficient chemoenzymatic strategies that combine the power of chemical synthesis and enzyme catalysis to make sialic acids, sialyl glycans, sialyl glycoconjugates, and their derivatives more accessible, enabling the efforts to explore their functions and applications. The Account starts with a brief description of the structural diversity and the functional importance of naturally occurring sialic acids and sialosides. The development of one-pot multienzyme (OPME) chemoenzymatic sialylation strategies is then introduced, highlighting its advantages in synthesizing structurally diverse sialosides with a sialyltransferase donor substrate engineering tactic. With the strategy, systematic access to sialosides containing different sialic acid forms with modifications at C3/4/5/7/8/9, various internal glycans, and diverse sialyl linkages is now possible. Also briefly described is the combination of the OPME sialylation strategy with bacterial sialidases for synthesizing sialidase inhibitors. With the goal of simplifying the product purification process for enzymatic glycosylation reactions, glycosphingolipids that contain a naturally existing hydrophobic tag are attractive targets for chemoenzymatic total synthesis. A user-friendly highly efficient chemoenzymatic strategy is developed which involves three main processes, including chemical synthesis of lactosyl sphingosine as a water-soluble hydrophobic tag-containing intermediate, OPME enzymatic extension of its glycan component with a single C18-cartridge purification of the product, followed by a facile chemical acylation reaction. The strategy allows the introduction of different sialic acid forms and diverse fatty acyl chains into the products. Gram-scale synthesis has been demonstrated. OPME sialylation has also been demonstrated for the chemoenzymatic synthesis of sialyl glycopeptides and in vitro enzymatic N-glycan processing for the formation of glycoproteins with disialylated biantennary complex-type N-glycans. For synthesizing human milk oligosaccharides (HMOs) which are glycans with a free reducing end, acceptor substrate engineering and process engineering strategies are developed, which involve the design of a hydrophobic tag that can be easily installed into the acceptor substrate to allow facile purification of the product from enzymatic reactions and can be conveniently removed in the final step to produce target molecules. The process engineering involves heat-inactivation of enzymes in the intermediate steps in multistep OPME reactions for the production of long-chain sialoside targets in a single reaction pot and with a single C18-cartridge purification process. In addition, a chemoenzymatic synthon strategy has been developed. It involves the design of a derivative of the sialyltransferase donor substrate precursor, which is tolerated by enzymes in OPME reactions, introduced to enzymatic products, and then chemically converted to the desired target structures in the final step. The chemoenzymatic synthon approach has been used together with the acceptor substrate engineering method in the synthesis of complex bacterial glycans containing sialic acids, legionaminic acids, and derivatives. The biocatalysts characterized and their engineered mutants developed by the Chen group are described, with highlights on synthetically useful enzymes. We anticipate further development of chemoenzymatic strategies and biocatalysts to enable exploration of the sialic acid space.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

What can entropy metrics tell us about the characteristics of ocular fixation trajectories?

In this study, we provide a detailed analysis of entropy measures calculated for fixation eye movement trajectories from the three different datasets. We employed six key metrics (Fuzzy, Increment, Sample, Gridded Distribution, Phase, and Spectral Entropies). We calculate these six metrics on three sets of fixations: (1) fixations from the GazeCom dataset, (2) fixations from what we refer to as the "Lund" dataset, and (3) fixations from our own research laboratory ("OK Lab" dataset). For each entropy measure, for each dataset, we closely examined the 36 fixations with the highest entropy and the 36 fixations with the lowest entropy. From this, it was clear that the nature of the information from our entropy metrics depended on which dataset was evaluated. These entropy metrics found various types of misclassified fixations in the GazeCom dataset. Two entropy metrics also detected fixation with substantial linear drift. For the Lund dataset, the only finding was that low spectral entropy was associated with what we call "bumpy" fixations. These are fixations with low-frequency oscillations. For the OK Lab dataset, three entropies found fixations with high-frequency noise which probably represent ocular microtremor. In this dataset, one entropy found fixations with linear drift. The between-dataset results are discussed in terms of the number of fixations in each dataset, the different eye movement stimuli employed, and the method of eye movement classification.

Competing Effects of Molecular Additives and Cross-Link Density on the Segmental Dynamics and Mechanical Properties of Cross-Linked Polymers

The introduction of molecular additives into thermosets often results in changes in their dynamics and mechanical properties that can have significant ramifications for diverse applications of this broad class of materials such as coatings, high-performance composites, etc. Currently, there is limited fundamental understanding of how such additives influence glass formation in these materials, a problem of broader significance in glass-forming materials. To address this fundamental problem, here, we employ a simplified coarse-grained (CG) model of a polymer network as a model of thermoset materials and then introduce a polymer additive having the same inherent rigidity and polymer-polymer interaction strength as the cross-linked polymer matrix. This energetically "neutral" or "self-plasticizing" additive model gives rise to non-trivial changes in the dynamics of glass formation and provides an important theoretical reference point for the technologically more important case of interacting additives. Based on this rather idealized model, we systematically explore the combined effect of varying the additive mass percentage (m) and cross-link density (c) on the segmental relaxation dynamics and mechanical properties of a model thermoset material with additives. We find that increasing the additive mass percentage m progressively decreases both the glass-transition temperature Tg and the fragility of glass formation, a trend opposite to increasing c so that these thermoset variables clearly have a competing effect on glass formation in these model materials. Moreover, basic mechanical properties (i.e., bulk, shear, and tensile moduli) likewise exhibit a competitive variation with the increase of m and c, which are strongly correlated with the Debye-Waller parameter ⟨u2⟩, a measure of material stiffness at a molecular scale. Our findings prove beneficial in the development of structure-property relationships for the cross-linked polymers, which could help guide the design of such network materials with tailored physical properties.

Targeted Polymersome Delivery of a Stapled Peptide for Drugging the Tumor Protein p53:BCL-2-Family Axis in Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) remains a formidable diagnosis in need of new treatment paradigms. In this work, we elucidated an opportunity for therapeutic synergy in DLBCL by reactivating tumor protein p53 with a stapled peptide, ATSP-7041, thereby priming cells for apoptosis and enhancing their sensitivity to BCL-2 family modulation with a BH3-mimetic, ABT-263 (navitoclax). While this combination was highly effective at activating apoptosis in DLBCL in vitro, it was highly toxic in vivo, resulting in a prohibitively narrow therapeutic window. We, therefore, developed a targeted nanomedicine delivery platform to maintain the therapeutic potency of this combination while minimizing its toxicity via packaging and targeted delivery of a stapled peptide. We developed a CD19-targeted polymersome using block copolymers of poly(ethylene glycol) disulfide linked to poly(propylene sulfide) (PEG-SS-PPS) for ATSP-7041 delivery into DLBCL cells. Intracellular delivery was optimized in vitro and validated in vivo by using an aggressive human DLBCL xenograft model. Targeted delivery of ATSP-7041 unlocked the ability to systemically cotreat with ABT-263, resulting in delayed tumor growth, prolonged survival, and no overt toxicity. This work demonstrates a proof-of-concept for antigen-specific targeting of polymersome nanomedicines, targeted delivery of a stapled peptide in vivo, and synergistic dual intrinsic apoptotic therapy against DLBCL via direct p53 reactivation and BCL-2 family modulation.

Ten simple rules for managing laboratory information

Information is the cornerstone of research, from experimental (meta)data and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems to transform this large information load into useful scientific findings.

Sustainable PET Waste Recycling: Labels from PET Water Bottles Used as a Catalyst for the Chemical Recycling of the Same Bottles

We report using a waste material, poly(ethylene terephthalate) (PET) water bottle labels, for the chemical recycling of the same PET water bottles. The solid fillers used for the manufacturing of the packaging labels were recovered by thermolysis in an electrical furnace at 600, 800, and 1000 °C with 13.5, 12.0, and 10.4 wt % recovery. Characterization of the solid residue showed the presence of calcium carbonate, calcium oxide, and titanium dioxide, which are typical fillers used for packaging film manufacturing, such as water bottle labels. These solid residues were then used as a catalyst for PET depolymerization by glycolysis, in which the catalyst recovered from bottle labels and shredded PET reacted in the presence of excess ethylene glycol at 200 °C. The reaction mixtures were analyzed for PET conversion and the yield of the bis(2-hydroxyethyl)terephthalate (BHET) monomer as the final product of the glycolysis reaction to determine the efficiency of the catalyst. Our results show that the catalyst prepared at 800 °C (Cat-800) has the best performance and provides a 100% PET conversion with a 95.8% BHET yield with a 1.0 wt % loading in 1.5 h. The catalyst from the PET water bottle labels is nontoxic, readily available, cost-effective, environmentally friendly, and can be used as a model for the self-sufficient chemical recycling of PET via glycolysis.

Monitoring Protein Complexation with Polyphosphazene Polyelectrolyte Using Automated Dynamic Light Scattering Titration and Asymmetric Flow Field Flow Fractionation and Protein Recognition Immunoassay

Polyphosphazenes represent a class of intrinsically flexible polyelectrolytes with potent immunoadjuvant activity, which is enabled through non-covalent self-assembly with antigenic proteins by charge complexation. The formation of supramolecular complexes between polyphosphazene adjuvant, poly[di(carboxylatophenoxy)phosphazene] (PCPP), and a model vaccine antigen, hen egg lysozyme, was studied under physiological conditions using automated dynamic light scattering titration, asymmetric flow field flow fractionation (AF4), enzyme-linked immunosorbent assay (ELISA), and fluorescent quenching methods. Three regimes of self-assembly were observed covering complexation of PCPP with lysozyme in the nano-scale range, multi-chain complexes, and larger aggregates with complexes characterized by a maximum loading of over six hundred protein molecules per PCPP chain and dissociation constant in the micromolar range (Kd = 7 × 10-6 mol/L). The antigenicity of PCPP bound lysozyme, when compared to equivalent lysozyme solutions, was largely retained for all complexes, but observed a dramatic reduction for heavily aggregated systems. Routes to control the complexation regimes with elevated NaCl or KCl salt concentrations indicate ion-specific effects, such that more smaller-size complexes are present at higher NaCl, counterintuitive with respect to PCPP solubility arguments. While the order of mixing shows a prominent effect at lower stoichiometries of mixing, higher NaCl salt reduces the effect all together.

Fundamental Equation of State for Fluid Tetrahydrofuran

An empirical fundamental equation of state in terms of the Helmholtz energy for tetrahydrofuran is presented. In the validity range from the triple-point temperature up to 550 K and pressures up to 600 MPa, the equation of state enables the calculation of all thermodynamic properties in the liquid, vapor, and super-critical regions including saturation states. Based on an extensive literature review, experimental data are represented within their experimental uncertainty. In the homogeneous liquid phase at atmospheric pressure, the uncertainty in density is 0.015 %, speed of sound is represented with an uncertainty of 0.03 %, and isobaric heat capacity has an uncertainty of 0.4 %. Isobaric heat capacities in the homogeneous vapor phase are described with an uncertainty of 0.2 %. Higher uncertainties occur above atmospheric pressure for all homogeneous properties. Depending on the temperature range, vapor pressure can be calculated with an uncertainty from 0.02 % to 3 %. The extrapolation behavior is evaluated, showing reasonable extrapolation behavior towards extreme conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10765-023-03258-3.

14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.

Mercury accumulation and biomarkers of exposure in two popular recreational fishes in Hawaiian waters

Mercury (Hg) exposure has not been examined in many recreational nearshore fish species that are commonly consumed around the Hawaiian Islands. Specific gene transcripts, such as metallothionein (MET) and thioredoxin reductase (TrxR), can be used to examine Hg exposure responses in aquatic organisms. This study measured total mercury (THg) in four species from two groups of Hawaiian nearshore fishes: giant trevally (Caranx ignobilis, n = 13), bluefin trevally (C. melampygus, n = 4), sharp jaw bonefish (Albula virgata, n = 2), and round jaw bonefish (A. glossodonta, n = 19). Total Hg accumulation and abundance profiles of MET and TrxR were evaluated for muscle, liver, and kidney tissues. Total Hg in round jaw bonefish and giant trevally tissues accumulated with length and calculated age. In round jaw bonefish tissues, mean THg was greater in kidney (1156 ng/g wet mass (wm)) than liver (339 ng/g wm) and muscle (330 ng/g wm). Giant trevally muscle (187 ng/g wm) and liver (277 ng/g wm) mean THg did not differ significantly. Fish species in this study were compared to commercial and local fish species with state and federal muscle tissue consumption advisories based on THg benchmarks developed by the U.S. Food and Drug Administration (FDA) and Environmental Protection Agency (EPA). Both bonefishes had mean muscle THg that exceeded benchmarks suggesting consumption advisories should be considered. MET transcript in round jaw bonefish kidney tissue and kidney THg exhibited a marginally significant positive correlation, while TrxR transcript in liver tissue negatively correlated with increasing liver THg. These results contribute to our understanding of Hg exposure associated health effects in fish.

Experiment and Theory in Concert To Unravel the Remarkable Electronic Properties of Na-Doped Eu11Zn4Sn2As12: A Layered Zintl Phase

Low-dimensional materials have unique optical, electronic, mechanical, and chemical properties that make them desirable for a wide range of applications. Nano-scaling materials to confine transport in at least one direction is a common method of designing materials with low-dimensional electronic structures. However, bulk materials give rise to low-dimensional electronic structures when bonding is highly anisotropic. Layered Zintl phases are excellent candidates for investigation due to their directional bonding, structural variety, and tunability. However, the complexity of the structure and composition of many layered Zintl phases poses a challenge for producing phase-pure bulk samples to characterize. Eu11Zn4Sn2As12 is a layered Zintl phase of significant complexity that is of interest for its magnetic, electronic, and thermoelectric properties. To prepare phase-pure Eu11-xNaxZn4Sn2As12, a binary EuAs phase was employed as a precursor, along with NaH. Experimental measurements reveal low thermal conductivity and a high Seebeck coefficient, while theoretical electronic structure calculations reveal a transition from a 3D to 2D electronic structure with increasing carrier concentration. Simulated thermoelectric properties also indicate anisotropic transport, and thermoelectric property measurements confirm the nonparabolicity of the relevant bands near the Fermi energy. Thermoelectric efficiency is known to improve as the dimensionality of the electronic structure is decreased, making this a promising material for further optimization and opening the door to further exploitation of layered Zintl phases with low-dimensional electronic structures for thermoelectric applications.

Harmonic dependence of thermal magnetic particle imaging

Advances in instrumentation and tracer materials are still required to enable sensitive, accurate, and localized in situ 3D temperature monitoring by magnetic particle imaging (MPI). We have developed a high-resolution magnetic particle imaging instrument and implemented a low-noise multi-harmonic lock-in detection method to observe and quantify temperature variations in iron oxide nanoparticle tracers using the harmonic ratio method for determining temperature. Using isolated harmonics for MPI and temperature imaging revealed an apparent dependence of imaging resolution on harmonic number. Thus, we present experimental and simulation studies to quantify the imaging resolution dependence on temperature and harmonic number, and directly validate the fundamental origin of MPI imaging resolution on harmonic number based on the concept of a harmonic point-spread-function.

Quantifying the Effect of Guest Binding on Host Environment

The environment around a host-guest complex is defined by intermolecular interactions between the complex, solvent molecules, and counterions. These interactions govern both the solubility of these complexes and the rates of reactions occurring within the host molecules and can be critical to catalytic and separation applications of host-guest systems. However, these interactions are challenging to detect using standard analytical chemistry techniques. Here, we quantify the hydration and ion pairing of a FeII4L4 coordination cage with a set of guest molecules having widely varying physicochemical properties. The impact of guest properties on host ion pairing and hydration was determined through microwave microfluidic measurements paired with principal component analysis (PCA). This analysis showed that introducing guest molecules into solution displaced counterions that were bound to the cage, and that the solvent solubility of the guest has the greatest impact on the solvent and ion-pairing dynamics surrounding the host. Specifically, we found that when we performed PCA of the measured equivalent circuit parameters and the solubility and dipole moment, we observed a high (>90%) explained variance for the first two principal components for each circuit parameter. We also observed that cage-counterion pairing is well-described by a single ion-pairing type, with a one-step reaction model independent of the type of cargo, and that the ion-pairing association constant is reduced for cargo with higher water solubility. Quantifying hydration and cage-counterion interactions is a critical step to building the next generation of design criteria for host-guest chemistries.

Kanatzidisite: A Natural Compound with Distinctive van der Waals Heterolayered Architecture

New minerals have long been a source of inspiration for the design and discovery. Many quantum materials, including superconductors, quantum spin liquids, and topological materials, have been unveiled through mineral samples with unusual structure types. In this report, we present kanatzidisite, a new naturally occurring material with formula [BiSbS3]2[Te2] and monoclinic symmetry (space group of P21/m) with lattice parameters a = 4.0021(5) Å, b = 3.9963(5) Å, c = 21.1009(10) Å, and β = 95.392(3)°. The mineral exhibits a unique structure consisting of alternating BiSbS3 double van der Waals layers and distorted [Te] square nets essentially forming an array of parallel zigzag Te chains. Our theoretical calculations suggest that the band structure of kanatzidisite may exhibit topological features characteristic of a Dirac semimetal.

Interdependence of social-ecological-technological systems in Phoenix, Arizona: consequences of an extreme precipitation event

Complex adaptive systems - such as critical infrastructures (CI) - are defined by their vast, multi-level interactions and emergent behaviors, but this elaborate web of interactions often conceals relationships. For instance, CI is often reduced to technological components, ignoring that social and ecological components are also embedded, leading to unintentional consequences from disturbance events. Analysis of CI as social-ecological-technological systems (SETS) can support integrated decision-making and increase infrastructure's capacity for resilience to climate change. We assess the impacts of an extreme precipitation event in Phoenix, AZ to identify pathways of disruption and feedback loops across SETS as presented in an illustrative causal loop diagram, developed through semi-structured interviews with researchers and practitioners and cross-validated with a literature review. The causal loop diagram consists of 19 components resulting in hundreds of feedback loops and cascading failures, with surface runoff, infiltration, and water bodies as well as power, water, and transportation infrastructures appearing to have critical roles in maintaining system services. We found that pathways of disruptions highlight potential weak spots within the system that could benefit from climate adaptation, and feedback loops may serve as potential tools to divert failure at the root cause. This method of convergence research shows potential as a useful tool to illustrate a broader perspective of urban systems and address the increasing complexity and uncertainty of the Anthropocene.

Supplementary Information

The online version contains supplementary material available at 10.1186/s43065-023-00085-6.

Controlled Glycolysis of Poly(ethylene terephthalate) to Oligomers under Microwave Irradiation Using Antimony(III) Oxide

We report here the production of higher-order oligomers from the glycolysis of poly(ethylene terephthalate) (PET) by using microwave irradiation in a controlled fashion, instead of its fully glycolyzed product, bis(2-hydroxyethyl)terephthalate (BHET). We show that different catalysts can generate either BHET as the ultimate glycolysis product or higher oligomers of PET under microwave irradiation. Depolymerization of waste PET with an average degree of polymerization (DP) of 417 from water bottles was performed in the presence of 0.25 wt % antimony(III) oxide (Sb2O3) as the catalyst at 240 °C and 400 W microwave power, resulting in an oligomer yield of 96.7% with an average DP of 37. Under these conditions, the conversion of PET to oligomers reached 100% in only 5 min at 240 °C (with a 10 min ramping time) and with a ethylene glycol to PET weight ratio of 2.5. In comparison, under the same reaction conditions, 0.04 wt % of zinc acetate (Zn(OAc)2), a well-known catalyst for PET glycolysis, produces only the BHET monomer in 96.3% yield. Our results demonstrated that by using Sb2O3, the same catalyst that is used extensively for PET synthesis from BHET, under microwave irradiation, the PET glycolysis can be controlled to produce higher PET oligomers as an alternative for a complete chemical depolymerization to the BHET monomer. These oligomers are more suitable for being used as additives for many applications and to produce high-quality second-generation products, including regenerated PET.

A Dual-Path Pulse-Echo Instrument for Liquid-Phase Speed of Sound and Measurements on p-Xylene and Four Halogenated-Olefin Refrigerants [R1234yf, R1234ze(E), R1233zd(E), and R1336mzz(Z)]

We describe an instrument to measure the speed of sound in liquids based on the dual-path, pulse-echo technique; it covers a temperature range of 228.15-423.15 K, with pressures of up to 93 MPa. It differs from similar instruments in the method of mounting the quartz-crystal transducer, a path-length ratio of 2.5:1, and automated data-collection protocols. The path-length difference was calibrated with measurements on high-purity propane. The performance of the instrument was verified by comparison with recent literature data on p-xylene. We present new liquid-phase measurements for the halogenated-olefin refrigerants 2,3,3,3-tetrafluoroprop-1-ene [R1234yf], trans-1,3,3,3-tetrafluoroprop-1-ene [R1234ze(E)], trans-1-chloro-3,3,3-trifluoroprop-1-ene [R1233zd(E)], and cis-1,1,1,4,4,4-hexafluorobut-2-ene [R1336mzz(Z)]. These measurements cover a combined temperature range of 230 to 420 K, with pressures of up to 50 MPa; these data are compared to literature data (where available) and multiproperty equations of state. The average relative expanded uncertainty in the speed of sound ranged from 0.035 to 0.088% for the different fluids.

Effects of heterozygous deletion of autism-related gene Cullin-3 in mice

Autism Spectrum Disorder (ASD) is a developmental disorder in which children display repetitive behavior, restricted range of interests, and atypical social interaction and communication. CUL3, coding for a Cullin family scaffold protein mediating assembly of ubiquitin ligase complexes through BTB domain substrate-recruiting adaptors, has been identified as a high-risk gene for autism. Although complete knockout of Cul3 results in embryonic lethality, Cul3 heterozygous mice have reduced CUL3 protein, demonstrate comparable body weight, and display minimal behavioral differences including decreased spatial object recognition memory. In measures of reciprocal social interaction, Cul3 heterozygous mice behaved similarly to their wild-type littermates. In area CA1 of hippocampus, reduction of Cul3 significantly increased mEPSC frequency but not amplitude nor baseline evoked synaptic transmission or paired-pulse ratio. Sholl and spine analysis data suggest there is a small yet significant difference in CA1 pyramidal neuron dendritic branching and stubby spine density. Unbiased proteomic analysis of Cul3 heterozygous brain tissue revealed dysregulation of various cytoskeletal organization proteins, among others. Overall, our results suggest that Cul3 heterozygous deletion impairs spatial object recognition memory, alters cytoskeletal organization proteins, but does not cause major hippocampal neuronal morphology, functional, or behavioral abnormalities in adult global Cul3 heterozygous mice.

Nanoscale Characterization of Photocurrent and Photovoltage in Polycrystalline Solar Cells

We investigate the role of grain structures in nanoscale carrier dynamics of polycrystalline solar cells. By using Kelvin probe force microscopy (KPFM) and near-field scanning photocurrent microscopy (NSPM) techniques, we characterize nanoscopic photovoltage and photocurrent patterns of inorganic CdTe and organic-inorganic hybrid perovskite solar cells. For CdTe solar cells, we analyze the nanoscale electric power patterns that are created by correlating nanoscale photovoltage and photocurrent maps on the same location. Distinct relations between the sample preparation conditions and the nanoscale photovoltaic properties of microscopic CdTe grain structures are observed. The same techniques are applied for characterization of a perovskite solar cell. It is found that a moderate amount of PbI₂ near grain boundaries leads to the enhanced photogenerated carrier collections at grain boundaries. Finally, the capabilities and the limitations of the nanoscale techniques are discussed.

Assessment of a Parachor Model for the Surface Tension of Binary Mixtures

We compiled an experimental database for the surface tension of binary mixtures containing a wide variety of fluids, from the chemical classes (water, alcohols, amines, ketones, linear and branched alkanes, naphthenes, aromatics, refrigerants, and cryogens). The resulting data set includes 65 pure fluids and 154 binary pairs with a total of 8205 points. We used this database to test the performance of a parachor model for the surface tension of binary mixtures. The model uses published correlations to determine the parachors of the pure fluids. The model has a single, constant binary interaction parameter for each pair that was found by fitting experimental mixture data. It can be also used in a predictive mode when the interaction parameters are set to zero. We present detailed comparisons on the performance of the model for both cases. In general, the parachor model in a predictive mode without fitted interaction parameters can predict the surface tension of binary mixtures of non-polar mixtures such as linear and branched alkanes, linear and branched alkanes with naphthenes, aromatics with aromatics, aromatics with naphthenes, and mixtures of linear alkanes of similar sizes with an average absolute percentage deviation of about 3 % or less. Polar mixtures of halocarbons with other halocarbons and also polar/nonpolar mixtures of alkanes with halocarbons could be modeled with an average absolute deviation of less than 0.35 mN·m-1 with the use of a binary interaction parameter. The parachor model even with a fitted binary interaction parameter performs poorly for mixtures of water and organic compounds and is not recommended.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10765-023-03216-z.

Hierarchical Bayesian non-response models for error rates in forensic black-box studies

Forensic science plays a critical role in the United States criminal legal system. Historically, however, most feature-based fields of forensic science, including firearms examination and latent print analysis, have not been shown to be scientifically valid. Recently, black-box studies have been proposed as a means of assessing whether these feature-based disciplines are valid, at least in terms of accuracy, reproducibility and repeatability. In these studies, forensic examiners frequently either do not respond to every test item or select an answer equivalent to 'don't know'. Current black-box studies do not account for these high levels of missingness in statistical analyses. Unfortunately, the authors of black-box studies typically do not share the data necessary to meaningfully adjust estimates for the high proportion of missing responses. Borrowing from work in the context of small area estimation, we propose the use of hierarchical Bayesian models that do not require auxiliary data to adjust for non-response. Using these models, we offer the first formal exploration of the impact that missingness is playing in error rate estimations reported in black-box studies. We show that error rates currently reported as low as 0.4% could actually be at least 8.4% in models accounting for non-response where inconclusive decisions are counted as correct, and over 28% when inconclusives are counted as missing responses. These proposed models are not the answer to the missingness problem in black-box studies. But with the release of auxiliary information, they can be the foundation for new methodologies to adjust for missingness in error rate estimations. This article is part of the theme issue 'Bayesian inference: challenges, perspectives, and prospects'.

Reactive Blending of Recycled Poly(ethylene terephthalate)/Recycled Polypropylene: Kinetics Modeling of Non-Isothermal Crystallization

Plastics were developed to change our world for the better. However, plastic pollution has become a serious global environmental crisis. Thermoplastic polyesters and polyolefins are among the most abundant plastic waste. This work presents an in-depth non-isothermal crystallization kinetics analysis of recycled post-consumer poly(ethylene terephthalate) (rPET) and recycled polypropylene (rPP) blends prepared through reactive compounding. The effect of pyromellitic dianhydride (PMDA) on crystallization kinetics and phase morphology of rPET/rPP blends was investigated by differential scanning calorimetry (DSC) and microscopy techniques. DSC results showed that increasing rPP content accelerated rPET crystallization while reducing crystallinity, which indicates the nucleation effect of the rPP phase in blends. Further, it was found that the incorporation of PMDA increased the degree of crystallinity during non-isothermal crystallization, even though the rate of crystallinity decreased slightly due to its restriction effects. The non-isothermal crystallization kinetics was analyzed based on the theoretical models developed by Jeziorny, Ozawa, Mo, and Tobin. The activation energy of the crystallization process derived from Kissinger, Takhor, and Augis-Bennett models was found to increase in rPET/rPP blends with increasing PMDA due to hindered dynamics of the system. Rheological measurements revealed that rPET melt viscosity is remarkably increased in the presence of PMDA and reactive blending with rPP relevant for processing. Moreover, nanomechanical mapping of the rPP phase dispersed in the rPET matrix demonstrated the broadening of the interfacial domains after reactive blending due to the branching effect of PMDA. Findings from this study are essential for the recycling/upcycling thermoplastics through non-isothermal fabrication processes, such as extrusion and injection molding, to mitigate the lack of sorting options.

Leveraging the Polymer Glass Transition to Access Thermally Switchable Shear Jamming Suspensions

Suspensions of polymeric nano- and microparticles are fascinating stress-responsive material systems that, depending on their composition, can display a diverse range of flow properties under shear, such as drastic thinning, thickening, and even jamming (reversible solidification driven by shear). However, investigations to date have almost exclusively focused on nonresponsive particles, which do not allow in situ tuning of the flow properties. Polymeric materials possess rich phase transitions that can be directly tuned by their chemical structures, which has enabled researchers to engineer versatile adaptive materials that can respond to targeted external stimuli. Reported herein are suspensions of (readily prepared) micrometer-sized polymeric particles with accessible glass transition temperatures (T _g) designed to thermally control their non-Newtonian rheology. The underlying mechanical stiffness and interparticle friction between particles change dramatically near T _g. Capitalizing on these properties, it is shown that, in contrast to conventional systems, a dramatic and nonmonotonic change in shear thickening occurs as the suspensions transition through the particles' T _g. This straightforward strategy enables the in situ turning on (or off) of the system's ability to shear jam by varying the temperature relative to T _g and lays the groundwork for other types of stimuli-responsive jamming systems through polymer chemistry.

Temperature Distribution in TaO x Resistive Switching Devices Assessed In Operando by Scanning Thermal Microscopy

Understanding the physical changes during electroformation and switching processes in transition-metal-oxide-based non-volatile memory devices is important for advancing this technology. Relatively few characteristics of these devices have been assessed in operando. In this work, we present scanning thermal microscopy measurements in vacuum on TaO _x -based memory devices electroformed in both positive and negative polarities and high- and low-resistance states. The observed surface temperature footprints of the filament showed higher peak temperatures and narrower temperature distributions when the top electrode served as the anode in the electroformation process. This is consistent with a model in which a hot spot is created by a gap in the conducting filament that forms closest to the anode. A similar behavior was seen on comparing the high-resistance state to the low-resistance state, with the low-resistance footprint showing a lower peak and a larger width, consistent with the gap disappearing when the device is switched from high resistance to low resistance.

Synthesis and Characterization of 15N5-Labeled Aflatoxin B1-Formamidopyrimidines and Aflatoxin B1-N7-Guanine from a Partial Double-Stranded Oligodeoxynucleotide as Internal Standards for Mass Spectrometric Measurements

Aflatoxin B₁ (AFB₁) exposure through contaminated food is a primary contributor to hepatocellular carcinogenesis worldwide. Hepatitis B viral infections in livers dramatically increase the carcinogenic potency of AFB₁ exposures. Liver cytochrome P450 oxidizes AFB₁ to the epoxide, which in turn reacts with N7-guanine in DNA, producing the cationic trans-8,9-dihydro-8-(N7-guanyl)-9-hydroxyaflatoxin B₁ adduct (AFB₁-N7-Gua). The opening of the imidazole ring of AFB₁-N7-Gua under physiological conditions causes the formation of the cis- and trans-diastereomers of 8,9-dihydro-8-(2,6-diamino-4-oxo-3,4-dihydropyrimid-5-yl-formamido)-9-hydroxyaflatoxin B₁ (AFB₁-FapyGua). These adducts primarily lead to G → T mutations, with AFB₁-FapyGua being significantly more mutagenic than AFB₁-N7-Gua. The unequivocal identification and accurate quantification of these AFB₁-Gua adducts as biomarkers are essential for a fundamental understanding and prevention of AFB₁-induced hepatocellular carcinogenesis. Among a variety of analytical techniques used for this purpose, liquid chromatography-tandem mass spectrometry, with the use of the stable isotope-labeled analogues of AFB₁-FapyGua and AFB₁-N7-Gua as internal standards, provides the greatest accuracy and sensitivity. cis-AFB₁-FapyGua-¹⁵N₅, trans-AFB₁-FapyGua-¹⁵N₅, and AFB₁-N7-Gua-¹⁵N₅ have been synthesized and used successfully as internal standards. However, the availability of these standards from either academic institutions or commercial sources ceased to exist. Thus, quantitative genomic data regarding AFB₁-induced DNA damage in animal models and humans remain challenging to obtain. Previously, AFB₁-N7-Gua-¹⁵N₅ was prepared by reacting AFB₁-exo-8,9-epoxide with the uniformly ¹⁵N₅-labeled DNA isolated from algae grown in a pure ¹⁵N-environment, followed by alkali treatment, resulting in the conversion of AFB₁-N7-Gua-¹⁵N₅ to AFB₁-FapyGua-¹⁵N₅. In the present work, we used a different and simpler approach to synthesize cis-AFB₁-FapyGua-¹⁵N₅, trans-AFB₁-FapyGua-¹⁵N₅, and AFB₁-N7-Gua-¹⁵N₅ from a partial double-stranded 11-mer Gua-¹⁵N₅-labeled oligodeoxynucleotide, followed by isolation and purification. We also show the validation of these ¹⁵N₅-labeled standards for the measurement of cis-AFB₁-FapyGua, trans-AFB₁-FapyGua, and AFB₁-N7-Gua in DNA of livers of AFB₁-treated mice.

A compendium of stable hotspots in the CHO genome

The use of targeted integration for industrial CHO cell line development currently requires significant upfront effort to identify genomic loci capable of supporting multigram per liter therapeutic protein production from a limited number of transgene copies. To address this barrier to widespread adoption, we characterized transgene expression from thousands of stable hotspots in the CHO genome using the Thousands of Reporters Integrated in Parallel high-throughput screening method. This genome-scale data set was used to define a limited set of epigenetic properties of hotspot regions with sizes on the order of 10 kb. Cell lines with landing pad integrations at eight retargeted hotspot candidates consistently exhibited higher transgene mRNA expression than a commercially viable hotspot in equivalent culture conditions. Initial benchmarking of NISTmAb and trastuzumab productivity from one of these hotspots yielded mAb productivities of approximately 0.7-2 g/L (qP range: 2.9-8.2 pg/cell/day) in small-scale fed-batches. These findings indicate the list of hotspot candidates identified here will be a valuable resource for targeted integration platform development within the CHO community.

The verification of wildland-urban interface fire evacuation models

This paper introduces a protocol for the verification of multi-physics wildfire evacuation models, including a set of tests used to ensure that the conceptual modelling representation of each modelling layer is accurately implemented, as well as the interactions between different modelling layers and sub-models (wildfire spread, pedestrian movement, traffic evacuation, and trigger buffers). This work presents a total of 24 verification tests, including (1) 4 tests related to pedestrians, (2) 15 tests for traffic evacuation, (3) 5 tests concerning the interaction between different modelling layers, along with 5 tests for wildfire spread and trigger buffers. The evacuation tests are organized in accordance with different core components related to evacuation modelling, namely Population, Pre-evacuation, Movement, Route/destination selection, Flow constraints, Events, Wildfire spread and Trigger buffers. A reporting template has also been developed to facilitate the application of the verification testing protocol. An example application of the testing protocol has been performed using an open wildfire evacuation modelling platform called WUI-NITY and its associated trigger buffer model k-PERIL. The verification testing protocol is deemed to improve the credibility of wildfire evacuation model results and stimulate future modelling efforts in this domain.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11069-023-05913-2.

TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology

BACKGROUND

Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF).

RESULTS

We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions.

CONCLUSIONS

TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.

Micrococcus spp. as a promising source for drug discovery: A review

Historically, bacteria of the phylum, Actinobacteria have been a very prominent source of bioactive compounds for drug discovery. Among the actinobacterial genera, Micrococcus has not generally been prioritized in the search for novel drugs. The bacteria in this genus are known to have very small genomes (generally < 3 Mb). Actinobacteria with small genomes seldom contain the well-characterized biosynthetic gene clusters such as those encoding polyketide synthases and nonribosomal peptide synthetases that current genome mining algorithms are optimized to detect. Nevertheless, there are many reports of substantial pharmaceutically relevant bioactivity of Micrococcus extracts. On the other hand, there are remarkably few descriptions of fully characterized and structurally elucidated bioactive compounds from Micrococcus spp. This review provides a comprehensive summary of the bioactivity of Micrococcus spp. that encompasses antibacterial, antifungal, cytotoxic, antioxidant, and anti-inflammatory activities. This review uncovers the considerable biosynthetic potential of this genus and highlights the need for a re-examination of these bioactive strains, with a particular emphasis on marine isolates, because of their potent bioactivity and high potential for encoding unique molecular scaffolds.

Roxborough Park Community Wildfire Evacuation Drill: Data Collection and Model Benchmarking

Wildfires are increasing in scale, frequency and longevity, and are affecting new locations as environmental conditions change. This paper presents a dataset collected during a community evacuation drill performed in Roxborough Park, Colorado (USA) in 2019. This is a wildland-urban interface community including approximately 900 homes. Data concerning several aspects of community response were collected through observations and surveys: initial population location, pre-evacuation times, route use, and arrival times at the evacuation assembly point. Data were used as inputs to benchmark two evacuation models that adopt different modelling approaches. The WUI-NITY platform and the Evacuation Management System model were applied across a range of scenarios where assumptions regarding pre-evacuation delays and the routes used were varied according to original data collection methods (and interpretation of the data generated). Results are mostly driven by the assumptions adopted for pre-evacuation time inputs. This is expected in communities with a low number of vehicles present on the road and relatively limited traffic congestion. The analysis enabled the sensitivity of the modelling approaches to different datasets to be explored, given the different modelling approaches adopted. The performance of the models were sensitive to the data employed (derived from either observations or self-reporting) and the evacuation phases addressed in them. This indicates the importance of monitoring the impact of including data in a model rather than simply on the data itself, as data affects models in different ways given the modelling methods employed. The dataset is released in open access and is deemed to be useful for future wildfire evacuation modelling calibration and validation efforts.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10694-023-01371-1.

Tackling imbalanced data in cybersecurity with transfer learning: a case with ROP payload detection

In recent years, deep learning gained proliferating popularity in the cybersecurity application domain, since when being compared to traditional machine learning methods, it usually involves less human efforts, produces better results, and provides better generalizability. However, the imbalanced data issue is very common in cybersecurity, which can substantially deteriorate the performance of the deep learning models. This paper introduces a transfer learning based method to tackle the imbalanced data issue in cybersecurity using return-oriented programming payload detection as a case study. We achieved 0.0290 average false positive rate, 0.9705 average F1 score and 0.9521 average detection rate on 3 different target domain programs using 2 different source domain programs, with 0 benign training data sample in the target domain. The performance improvement compared to the baseline is a trade-off between false positive rate and detection rate. Using our approach, the total number of false positives is reduced by 23.16%, and as a trade-off, the number of detected malicious samples decreases by 0.68%.

Generative Adversarial Networks and Mixture Density Networks-Based Inverse Modeling for Microstructural Materials Design

There are two broad modeling paradigms in scientific applications: forward and inverse. While forward modeling estimates the observations based on known causes, inverse modeling attempts to infer the causes given the observations. Inverse problems are usually more critical as well as difficult in scientific applications as they seek to explore the causes that cannot be directly observed. Inverse problems are used extensively in various scientific fields, such as geophysics, health care and materials science. Exploring the relationships from properties to microstructures is one of the inverse problems in material science. It is challenging to solve the microstructure discovery inverse problem, because it usually needs to learn a one-to-many nonlinear mapping. Given a target property, there are multiple different microstructures that exhibit the target property, and their discovery also requires significant computing time. Further, microstructure discovery becomes even more difficult because the dimension of properties (input) is much lower than that of microstructures (output). In this work, we propose a framework consisting of generative adversarial networks and mixture density networks for inverse modeling of structure-property linkages in materials, i.e., microstructure discovery for a given property. The results demonstrate that compared to baseline methods, the proposed framework can overcome the above-mentioned challenges and discover multiple promising solutions in an efficient manner.

Synthesizing ultrafast optical pulses with arbitrary spatiotemporal control

The ability to control the instantaneous state of light, from high-energy pulses down to the single-photon level, is an indispensable requirement in photonics. This has, for example, facilitated spatiotemporal probing and coherent control of ultrafast light-matter interactions, and enabled capabilities such as generation of exotic states of light with complexity, or at wavelengths, that are not easily accessible. Here, by leveraging the multifunctional control of light at the nanoscale offered by metasurfaces embedded in a Fourier transform setup, we present a versatile approach to synthesize ultrafast optical transients with arbitrary control over its complete spatiotemporal evolution. Our approach, supporting an ultrawide bandwidth with simultaneously high spectral and spatial resolution, enables ready synthesis of complex states of structured space-time wave packets. We expect our results to offer unique capabilities in coherent ultrafast light-matter interactions and facilitate applications in microscopy, communications, and nonlinear optics.

Hamiltonian engineering of spin-orbit-coupled fermions in a Wannier-Stark optical lattice clock

Engineering a Hamiltonian system with tunable interactions provides opportunities to optimize performance for quantum sensing and explore emerging phenomena of many-body systems. An optical lattice clock based on partially delocalized Wannier-Stark states in a gravity-tilted shallow lattice supports superior quantum coherence and adjustable interactions via spin-orbit coupling, thus presenting a powerful spin model realization. The relative strength of the on-site and off-site interactions can be tuned to achieve a zero density shift at a "magic" lattice depth. This mechanism, together with a large number of atoms, enables the demonstration of the most stable atomic clock while minimizing a key systematic uncertainty related to atomic density. Interactions can also be maximized by driving off-site Wannier-Stark transitions, realizing a ferromagnetic to paramagnetic dynamical phase transition.

Solution properties of spherical gold nanoparticles with grafted DNA chains from simulation and theory

There has been a rapidly growing interest in the use of functionalized Au nanoparticles (NPs) as platforms in multiple applications in medicine and manufacturing. The sensing and targeting characteristics of these NPs, and the realization of precisely organized structures in manufacturing applications using such NPs, depend on the control of their surface functionalization. NP functionalization typically takes the form of polymer grafted layers, and a detailed knowledge of the chemical and structural properties of these layers is required to molecularly engineer the particle characteristics for specific applications. However, the prediction and experimental determination of these properties to enable the rational engineering of these particles is a persistent problem in the development of this class of materials. To address this situation, molecular dynamic simulations were performed based on a previously established coarse-grained single-stranded DNA (ssDNA) model to determine basic solution properties of model ssDNA-grafted NP-layers under a wide range of conditions. In particular, we emphasize the calculation of the hydrodynamic radius for ssDNA-grafted Au NPs as a function of structural parameters such as ssDNA length, NP core size, and surface coverage. We also numerically estimate the radius of gyration and the intrinsic viscosity of these NPs, which in combination with hydrodynamic radius estimates, provide valuable information about the fluctuating structure of the grafted polymer layers. We may then understand the origin of the commonly reported variation in effective NP "size" by different measurement methods, and then exploit this information in connection to material design and characterization in connection with the ever-growing number of applications utilizing polymer-grafted NPs.

Effects of chemical and autoclave sterilization treatments on medical personal protective equipment made of nonwoven polypropylene fibers for recycling

Medical personal protective equipment (PPE) made from nonwoven thermoplastic fibers has been intensively used, resulting in a large amount of biohazardous waste. Sterilization is indispensable before recycling medical waste. The aim of this work is to evaluate the effects of the decontamination treatments and help properly recycle the PPE materials. The study investigated the effects of three disinfection treatments (NaClO, H₂O₂, and autoclave) on chemical composition, molecular weight, thermal properties, crystallinity, crystallization kinetics, and mechanical tension of three types of PPE (Gown #1, Gown #2, and Wrap) made of isotactic polypropylene fibers. The chemical compositions of the materials were not evidently affected by any of the treatments. However, the M_w of the polymers decreased about 2–7% after the treatments, although the changes were not statistically significant. The treatments barely affected the melting and crystallization temperatures and the maximum force at break, but they tended to elevate the thermal degradation temperatures. Although the treatments did not notably influence the crystallinities, crystallization rates and crystal growths were altered based on the Avrami model regression. Since the detected changes would not significantly affect polymer processing, the treated materials were suitable for recycling. Meanwhile, evident differences in the three types of raw materials were recorded. Their initial properties fluctuated notably, and they often behaved differently during the treatments, which could affect recycling operation. Recyclers should test and sort the raw materials to assure product quality. The results in this study provide fundamental data for recycling medical PPE to reduce its environmental footprint.

Predicting Lattice Vibrational Frequencies Using Deep Graph Neural Networks

Lattice vibrational frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational calculation of vibrational frequencies using density functional theory methods is computationally too demanding for large number of samples in materials screening. Here we propose a deep graph neural network based algorithm for predicting crystal vibrational frequencies from crystal structures. Our algorithm addresses the variable dimension of vibrational frequency spectrum using the zero padding scheme. Benchmark studies on two data sets with 15,000 mixed-structure and 35,552 rhombohedra samples show that the aggregated R ² scores of the prediction reach 0.554 and 0.724. We also evaluate the structural transferability by predicting the vibration frequencies for 239 individual cubic target structures. The R ² scores for more than 40% of the targets are greater than 0.8 and can reach as high as 0.98 for the model trained with mixed samples, while the average mean absolute error is 43.69 Thz showing low transferability across structure types. Our work demonstrates the capability of deep graph neural networks to learn to predict lattice vibration frequency when sufficient number of training samples are available.

Chromatography at -30 °C for Reduced Back-Exchange, Reduced Carryover, and Improved Dynamic Range for Hydrogen-Deuterium Exchange Mass Spectrometry

For hydrogen-deuterium exchange mass spectrometry (HDX-MS) to have an increased role in quality control of biopharmaceuticals, H for D back-exchange occurring during protein analyses should be minimized to promote greater reproducibility. Standard HDX-MS analysis systems that digest proteins and separate peptides at pH 2.7 and 0 °C can lose >30% of the deuterium marker within 15 min of sample injection. This report describes the architecture and performance of a dual-enzyme, HDX-MS instrument that conducts liquid chromatography (LC) separations at subzero temperature, thereby reducing back-exchange and supporting longer LC separations with improved chromatographic resolution. LC separations of perdeuterated, fully reduced, iodoacetamide-treated BSA protein digest standard peptides were performed at 0, -10, -20, and -30 °C in ethylene glycol (EG)/H₂O mixtures. Analyses conducted at -20 and -30 °C produced similar results. After subtracting for deuterium retained in arginine side chains, the average peptide eluted during a 40 min gradient contained ≈16% more deuterium than peptides eluted with a conventional 8 min gradient at 0 °C. A subset of peptides exhibited ≈26% more deuterium. Although chromatographic peaks shift with EG concentration and temperature, the apparatus elutes unbroadened LC peaks. Electrospray ion intensity does not decline with increasing EG fraction. To minimize bias from sample carryover, the fluidic circuits allow flush and backflush cleaning of all enzyme and LC columns. The system can perform LC separations and clean enzyme columns simultaneously. Temperature zones are controlled ±0.058 °C. The potential of increased sensitivity by mixing acetonitrile with the analytical column effluent was also examined.

Polyoxocationic antimony oxide cluster with acidic protons

The success and continued expansion of research on metal-oxo clusters owe largely to their structural richness and wide range of functions. However, while most of them known to date are negatively charged polyoxometalates, there is only a handful of cationic ones, much less functional ones. Here, we show an all-inorganic hydroxyiodide [H_10.7Sb_32.1O₄₄][H_2.1Sb_2.1I₈O₆][Sb_0.76I₆]₂·25H₂O (HSbOI), forming a face-centered cubic structure with cationic Sb₃₂O₄₄ clusters and two types of anionic clusters in its interstitial spaces. Although it is submicrometer in size, electron diffraction tomography of HSbOI allowed the construction of the initial structural model, followed by powder Rietveld refinement to reach the final structure. The cationic cluster is characterized by the presence of acidic protons on its surface due to substantial Sb³⁺ deficiencies, which enables HSbOI to serve as an excellent solid acid catalyst. These results open up a frontier for the exploration and functionalization of cationic metal-oxo clusters containing heavy main group elements.

SHBG, Bone Mineral Density, and Physical Function Among Injection Drug Users With and Without HIV and HCV

CONTEXT

Sex hormone-binding globulin (SHBG) is a glycoprotein that regulates the bioavailability of sex hormones and is higher in people with HIV (PWH) and hepatitis C virus (HCV). SHBG is associated with aging-related diseases, including osteoporosis and frailty in the general population. However, the relationship between SHBG concentration and bone mineral density (BMD) and physical function among PWH and HCV is unclear.

OBJECTIVE

This study aimed to evaluate the association between chronic infection with HIV and HCV and SHBG, and to assess the relationship of circulating SHBG concentrations with low BMD, physical function impairment, and frailty.

METHODS

A cross-sectional study was conducted of 278 HCV-exposed (HCV antibody positive) adults enrolled with and without HIV and HCV from the AIDS Linked to the IntraVenous Experience cohort study into 4 groups: HCV-/HIV-, HCV-/HIV+, HCV+/HIV-, and HCV+/HIV+. We evaluated the association between SHBG concentrations and grip strength, gait speed, Short Physical Performance Battery score, frailty (Fried Frailty Phenotype), and BMD (lumbar spine, total hip, and femoral neck T-score) by using adjusted multivariable regression stratified by sex.

RESULTS

SHBG concentrations were higher in women, in those with HIV RNA greater than 400 copies/mL (P = .02) and HCV RNA greater than 15 IU/mL (P < .001). In adjusted models, higher SHBG concentrations among women were statistically significantly associated with lower grip strength (-0.43 [95% CI, -0.77 to -0.081] kg/10 nmol/L, P < .05), higher odds of frailty (odds ratio, 1.49 [95% CI, 1.07 to 2.08], P < .05), and lower T-scores at the lumbar spine (-0.070 [95% CI, -0.15 to -0.001] SD/10 nmol/L T-score BMD, P < .05). Similar associations were not observed among men.

CONCLUSION

Higher SHBG concentrations are associated with the presence of HIV and HCV viremia. Among women, but not men, higher SHBG concentrations were associated with lower grip strength, higher odds of frailty, and lower lumbar spine BMD. The underlying mechanisms of these associations require further investigation.

Organosolv Lignin Improved Thermoplastic Elastomeric Behavior of Polyethylene/Polyisoprene Blend

Thermoplastic elastomers are considered the fastest-growing elastomers in recent years because of their thermomechanical recyclability, in contrast to traditional thermoset rubbers. Polyolefins such as low-density polyethylene (LDPE) show low mechanical properties, particularly poor elongation when compared with an elastomer or rubber. In this study, LDPE resin is converted to highly ductile rubber-like materials with high elongation and low modulus properties on blending with polyisoprene rubber (IR), followed by treating with dicumyl peroxide as a curing agent and organosolv lignin as an additive. The technique of high shear melt-mixing, in conjunction with vulcanization or crosslinking using organic peroxide, is used to develop hybrid materials based on the LDPE/IR blend at a 70/30 mass ratio, where LDPE is replaced partly with lignin. Various characteristics such as tensile, viscoelasticity, melt flow, crystallinity, and phase morphology of the materials are analyzed. As expected, vulcanization with peroxide can improve the mechanical performance of the LDPE/IR blends, which is further improved with the application of lignin (2 to 5 wt. %), particularly tensile strain is profoundly increased. For example, the average values of the tensile strength, the modulus, and the ultimate elongation of neat LDPE resin are 7.8 MPa, 177 MPa, and 62%, respectively, and those of LDPE/IR/lignin/DCP 65/30/05/2 are 8.1 MPa, 95 MPa, and 238%, respectively. It indicates that the application of lignin/DCP has a profound effect on improving the ductility and elastomeric characteristics of the materials; thus, this material can have the potential to replace traditional rubber products.

Tailoring sub-3.3 Å ultramicropores in advanced carbon molecular sieve membranes for blue hydrogen production

Carbon molecular sieve (CMS) membranes prepared by carbonization of polymers containing strongly size-sieving ultramicropores are attractive for high-temperature gas separations. However, polymers need to be carbonized at extremely high temperatures (900° to 1200°C) to achieve sub-3.3 Å ultramicroporous channels for H₂/CO₂ separation, which makes them brittle and impractical for industrial applications. Here, we demonstrate that polymers can be first doped with thermolabile cross-linkers before low-temperature carbonization to retain the polymer processability and achieve superior H₂/CO₂ separation properties. Specifically, polybenzimidazole (PBI) is cross-linked with pyrophosphoric acid (PPA) via H bonding and proton transfer before carbonization at ≤600°C. The synergistic PPA doping and subsequent carbonization of PBI increase H₂ permeability from 27 to 140 Barrer and H₂/CO₂ selectivity from 15 to 58 at 150°C, superior to state-of-the-art polymeric materials and surpassing Robeson's upper bound. This study provides a facile and effective way to tailor subnanopore size and porosity in CMS membranes with desirable molecular sieving ability.

Seasonal changes in the expression of insulin-like androgenic hormone (IAG) in the androgenic gland of the Jonah crab, Cancer borealis

Harvesting the adult male Jonah crab, Cancer borealis, mainly based on the size, has become an economically significant fishery, particularly in the Southern New England region of the US since 2000. Many decapod crustacean fisheries including C. borealis rely on harvesting adult males. Understanding the size related-sexual maturity and the seasonal changes in male reproductive activity is critical for sustainable management. In other decapods, an insulin-like hormone produced by the male-specific androgenic gland (AG), called insulin-like androgenic gland factor (IAG), plays an essential role in sexual maturity. Specifically IAG is involved in developing male primary and secondary sexual characteristics including spermatogenesis. This study aimed first to identify the IAG, then examine if season influences IAG expression in C. borealis males. Finally, the AG transcriptome was used to test if eyestalk neuropeptides regulate IAG levels via an endocrine axis between the two endocrine tissues as established in other crustaceans. The full-length CabIAG sequence is 928 nucleotides long, encoding a 151 amino acid deduced sequence. The CabIAG identified from the AG transcriptome after eyestalk ablation was the most highly expressed gene and accounted for up to 25% of transcripts, further confirming the presence of an endocrine axis between the androgenic gland and eyestalk ganglia. This gene expression was exclusive in male C. borealis AG. The transcriptomic analysis also revealed strong upregulation of the PPOAE transcript and downregulation of proteolytic enzymes. The CabIAG levels differ by season, increasing AG activity in fall and possibly coinciding with high mating activity. The timing of increased AG activity correlating to mating with females should be considered for better stock management for the C. borealis population.

An explainable and efficient deep learning framework for video anomaly detection

Deep learning-based video anomaly detection methods have drawn significant attention in the past few years due to their superior performance. However, almost all the leading methods for video anomaly detection rely on large-scale training datasets with long training times. As a result, many real-world video analysis tasks are still not applicable for fast deployment. On the other hand, the leading methods cannot provide interpretability due to the uninterpretable feature representations hiding the decision-making process when anomaly detection models are considered as a black box. However, the interpretability for anomaly detection is crucial since the corresponding response to the anomalies in the video is determined by their severity and nature. To tackle these problems, this paper proposes an efficient deep learning framework for video anomaly detection and provides explanations. The proposed framework uses pre-trained deep models to extract high-level concept and context features for training denoising autoencoder (DAE), requiring little training time (i.e., within 10 s on UCSD Pedestrian datasets) while achieving comparable detection performance to the leading methods. Furthermore, this framework presents the first video anomaly detection use of combing autoencoder and SHapley Additive exPlanations (SHAP) for model interpretability. The framework can explain each anomaly detection result in surveillance videos. In the experiments, we evaluate the proposed framework's effectiveness and efficiency while also explaining anomalies behind the autoencoder's prediction. On the USCD Pedestrian datasets, the DAE achieved 85.9% AUC with a training time of 5 s on the USCD Ped1 and 92.4% AUC with a training time of 2.9 s on the UCSD Ped2.

The materials tetrahedron has a "digital twin"

ABSTRACT

For over three decades, the materials tetrahedron has captured the essence of materials science and engineering with its interdependent elements of processing, structure, properties, and performance. As modern computational and statistical techniques usher in a new paradigm of data-intensive scientific research and discovery, the rate at which the field of materials science and engineering capitalizes on these advances hinges on collaboration between numerous stakeholders. Here, we provide a contemporary extension to the classic materials tetrahedron with a dual framework-adapted from the concept of a "digital twin"-which offers a nexus joining materials science and information science. We believe this high-level framework, the materials-information twin tetrahedra (MITT), will provide stakeholders with a platform to contextualize, translate, and direct efforts in the pursuit of propelling materials science and technology forward.

IMPACT STATEMENT

This article provides a contemporary reimagination of the classic materials tetrahedron by augmenting it with parallel notions from information science. Since the materials tetrahedron (processing, structure, properties, performance) made its first debut, advances in computational and informational tools have transformed the landscape and outlook of materials research and development. Drawing inspiration from the notion of a digital twin, the materials-information twin tetrahedra (MITT) framework captures a holistic perspective of materials science and engineering in the presence of modern digital tools and infrastructures. This high-level framework incorporates sustainability and FAIR data principles (Findable, Accessible, Interoperable, Reusable)-factors that recognize how systems impact and interact with other systems-in addition to the data and information flows that play a pivotal role in knowledge generation. The goal of the MITT framework is to give stakeholders from academia, industry, and government a communication tool for focusing efforts around the design, development, and deployment of materials in the years ahead.