A Cancer Nanovaccine for Co-Delivery of Peptide Neoantigens and Optimized Combinations of STING and TLR4 Agonists

Immune checkpoint blockade (ICB) has revolutionized cancer treatment and led to complete and durable responses, but only for a minority of patients. Resistance to ICB can largely be attributed to insufficient number and/or function of antitumor CD8+ T cells in the tumor microenvironment. Neoantigen targeted cancer vaccines can activate and expand the antitumor T cell repertoire, but historically, clinical responses have been poor because immunity against peptide antigens is typically weak, resulting in insufficient activation of CD8+ cytotoxic T cells. Herein, we describe a nanoparticle vaccine platform that can overcome these barriers in several ways. First, the vaccine can be reproducibly formulated using a scalable confined impingement jet mixing method to coload a variety of physicochemically diverse peptide antigens and multiple vaccine adjuvants into pH-responsive, vesicular nanoparticles that are monodisperse and less than 100 nm in diameter. Using this approach, we encapsulated synergistically acting adjuvants, cGAMP and monophosphoryl lipid A (MPLA), into the nanocarrier to induce a robust and tailored innate immune response that increased peptide antigen immunogenicity. We found that incorporating both adjuvants into the nanovaccine synergistically enhanced expression of dendritic cell costimulatory markers, pro-inflammatory cytokine secretion, and peptide antigen cross-presentation. Additionally, the nanoparticle delivery increased lymph node accumulation and uptake of peptide antigen by dendritic cells in the draining lymph node. Consequently, nanoparticle codelivery of peptide antigen, cGAMP, and MPLA enhanced the antigen-specific CD8+ T cell response and delayed tumor growth in several mouse models. Finally, the nanoparticle platform improved the efficacy of ICB immunotherapy in a murine colon carcinoma model. This work establishes a versatile nanoparticle vaccine platform for codelivery of peptide neoantigens and synergistic adjuvants to enhance responses to cancer vaccines.

Integrating Cardiovascular Engineering and Biofluid Mechanics in High School Science, Technology, Engineering, and Mathematics Education: An Experiential Approach

Science, technology, engineering, and mathematics (STEM) education workshops and programs play a key role in promoting early exposure to scientific applications and questions. Such early engagement leads to growing not only passion and interest in science, but it also leads to skill development through hands-on learning and critical thinking activities. Integrating physiology and engineering together is necessary especially to promote health technology awareness and introduce the young generation to areas where innovation is needed and where there is no separation between health-related matters and engineering methods and applications. To achieve this, we created a workshop aimed at K-12 (grades 9-11) students as part of the Summer Youth Programs at Michigan Technological University. The aim of this workshop was to expose students to how engineering concepts and methods translate into health- and medicine-related applications and cases. The program consisted of a total of 15 h and was divided into three sections over a period of 2 weeks. It involved a combination of theoretical and hands-on guided activities that we developed. At the end of the workshop, the students were provided a lesson or activity-specific assessment sheet and a whole workshop-specific assessment sheet to complete. They rated the programs along a 1-5 Likert scale and provided comments and feedback on what can be improved in the future. Students rated hands-on activities the highest in comparison with case studies and individual independent research. Conclusively, this STEM summer-youth program was a successful experience with many opportunities that will contribute to the continued improvement of the workshop in the future.

A multi-scale lifecycle and technoeconomic framework for higher education fleet electrification

Transportation accounts for one-quarter of all energy related greenhouse gas emissions. As it pertains to transport electrification, higher education institutions-such as universities-can model solutions that affect broader society. Despite this, higher education's role in fleet electrification adoption has been understudied. We, therefore, modeled an archetypical higher education institution to analyze the carbon and economic payback periods of three electrification scenarios (Business-as-Usual, Targeted Electrification, and Full Electrification) using a cradle-to-grave lifecycle and technoeconomic approach. Given the archetypical higher education institution fleet of 368 vehicles, results show an economic ratio plateau point of about 8 years at 20 fuel-based cars replaced by electric vehicles and a carbon payback period peak of roughly 10 months at 50 fuel-based cars replaced. We then performed a multi-scalar analysis by leveraging implementation theory. We find that higher education institutions that adhere to the tenets of implementation theory are poised to be pro-environmental change agents in many regions and countries. The methods and findings herein can be adapted to other institutions, regardless of fleet size, and can bolster relevant decision-making outcomes now.

Opportunities and Challenges for Machine Learning-Assisted Enzyme Engineering

Enzymes can be engineered at the level of their amino acid sequences to optimize key properties such as expression, stability, substrate range, and catalytic efficiency-or even to unlock new catalytic activities not found in nature. Because the search space of possible proteins is vast, enzyme engineering usually involves discovering an enzyme starting point that has some level of the desired activity followed by directed evolution to improve its "fitness" for a desired application. Recently, machine learning (ML) has emerged as a powerful tool to complement this empirical process. ML models can contribute to (1) starting point discovery by functional annotation of known protein sequences or generating novel protein sequences with desired functions and (2) navigating protein fitness landscapes for fitness optimization by learning mappings between protein sequences and their associated fitness values. In this Outlook, we explain how ML complements enzyme engineering and discuss its future potential to unlock improved engineering outcomes.

New Features of Laboratory-Generated EPFRs from 1,2-Dichlorobenzene (DCB) and 2-Monochlorophenol (MCP)

The present research is primarily focused on investigating the characteristics of environmentally persistent free radicals (EPFRs) generated from commonly recognized aromatic precursors, namely, 1,2-dichlorobenzene (DCB) and 2-monochlorophenol (MCP), within controlled laboratory conditions at a temperature of 230 °C, termed as DCB230 and MCP230 EPFRs, respectively. An intriguing observation has emerged during the creation of EPFRs from MCP and DCB utilizing a catalyst 5% CuO/SiO2, which was prepared through various methods. A previously proposed mechanism, advanced by Dellinger and colleagues (a conventional model), postulated a positive correlation between the degree of hydroxylation on the catalyst's surface (higher hydroxylated, HH and less hydroxylated, LH) and the anticipated EPFR yields. In the present study, this correlation was specifically confirmed for the DCB precursor. Particularly, it was observed that increasing the degree of hydroxylation at the catalyst's surface resulted in a greater yield of EPFRs for DCB230. The unexpected finding was the indifferent behavior of MCP230 EPFRs to the surface morphology of the catalyst, i.e., no matter whether copper oxide nanoparticles are distributed densely, sparsely, or completely agglomerated. The yields of MCP230 EPFRs remained consistent regardless of the catalyst type or preparation protocol. Although current experimental results confirm the early model for the generation of DCB EPFRs (i.e., the higher the hydroxylation is, the higher the yield of EPFRs), it is of utmost importance to closely explore the heterogeneous alternative mechanism(s) responsible for generating MCP230 EPFRs, which may run parallel to the conventional model. In this study, detailed spectral analysis was conducted using the EPR technique to examine the nature of DCB230 EPFRs and the aging phenomenon of DCB230 EPFRs while they exist as surface-bound o-semiquinone radicals (o-SQ) on copper sites. Various aspects concerning bound radicals were explored, including the hydrogen-bonding tendencies of o-semiquinone (o-SQ) radicals, the potential reversibility of hydroxylation processes occurring on the catalyst's surface, and the analysis of selected EPR spectra using EasySpin MATLAB. Furthermore, alternative routes for EPFR generation were thoroughly discussed and compared with the conventional model.

An Affordable Topography-Based Protocol for Assigning a Residue's Character on a Hydropathy (PARCH) Scale

The hydropathy of proteins or quantitative assessment of protein-water interactions has been a topic of interest for decades. Most hydropathy scales use a residue-based or atom-based approach to assign fixed numerical values to the 20 amino acids and categorize them as hydrophilic, hydroneutral, or hydrophobic. These scales overlook the protein's nanoscale topography, such as bumps, crevices, cavities, clefts, pockets, and channels, in calculating the hydropathy of the residues. Some recent studies have included protein topography in determining hydrophobic patches on protein surfaces, but these methods do not provide a hydropathy scale. To overcome the limitations in the existing methods, we have developed a Protocol for Assigning a Residue's Character on the Hydropathy (PARCH) scale that adopts a holistic approach to assigning the hydropathy of a residue. The parch scale evaluates the collective response of the water molecules in the protein's first hydration shell to increasing temperatures. We performed the parch analysis of a set of well-studied proteins that include the following─enzymes, immune proteins, and integral membrane proteins, as well as fungal and virus capsid proteins. Since the parch scale evaluates every residue based on its location, a residue may have very different parch values inside a crevice versus a surface bump. Thus, a residue can have a range of parch values (or hydropathies) dictated by the local geometry. The parch scale calculations are computationally inexpensive and can compare hydropathies of different proteins. The parch analysis can affordably and reliably aid in designing nanostructured surfaces, identifying hydrophilic and hydrophobic patches, and drug discovery.

Dependence of the Metal-Insulator-Semiconductor Schottky Barrier Height on Insulator Composition

The effects of different high-κ tunnel oxides on the metal-insulator-semiconductor Schottky barrier height (ΦB) were systematically investigated. While these high-κ interlayers have been previously observed to affect ΦB, there has never been a clear consensus as to why this ΦB modulation occurs. Changes in ΦB were measured when adding 0.5 nm of seven different high-κ oxides to n-Si/Ni contacts with a thin native silicon oxide also present. Depending on the high-κ oxide composition and ΦB measurement technique, increases in ΦB up to 0.4 eV and decreases up to 0.2 eV with a high-κ introduction were measured. The results were compared to several different hypotheses regarding the effects of tunnel oxides on ΦB. The experimental data correlated most closely with the model of a dipole formed at the SiOx/high-κ interface due to the difference in the oxygen areal density between the two oxides. Knowledge of this relationship will aid in the design of Schottky and ohmic contacts by providing criteria to predict the effects of different oxide stacks on ΦB.

Cluster-Enhanced Nanopore Sensing of Ovarian Cancer Marker Peptides in Urine

The development of novel methodologies that can detect biomarkers from cancer or other diseases is both a challenge and a need for clinical applications. This partly motivates efforts related to nanopore-based peptide sensing. Recent work has focused on the use of gold nanoparticles for selective detection of cysteine-containing peptides. Specifically, tiopronin-capped gold nanoparticles, trapped in the cis-side of a wild-type α-hemolysin nanopore, provide a suitable anchor for the attachment of cysteine-containing peptides. It was recently shown that the attachment of these peptides onto a nanoparticle yields unique current signatures that can be used to identify the peptide. In this article, we apply this technique to the detection of ovarian cancer marker peptides ranging in length from 8 to 23 amino acid residues. It is found that sequence variability complicates the detection of low-molecular-weight peptides (<10 amino acid residues), but higher-molecular-weight peptides yield complex, high-frequency current fluctuations. These fluctuations are characterized with chi-squared and autocorrelation analyses that yield significantly improved selectivity when compared to traditional open-pore analysis. We demonstrate that the technique is capable of detecting the only two cysteine-containing peptides from LRG-1, an emerging protein biomarker, that are uniquely present in the urine of ovarian cancer patients. We further demonstrate the detection of one of these LRG-1 peptides spiked into a sample of human female urine.

Impact of Structural Changes on Energy Transfer in the Anion-Engineered Re3+:Y2O3 Through Low-Temperature Synthesis Approach

Anion engineering has proven to be an effective strategy to tailor the physical and chemical properties of metal oxides by modifying their existing crystal structures. In this work, a low-temperature synthesis for rare earth (RE)-doped Y2O2SO4 and Y2O2S was developed via annealing of Y(OH)3 intermediates in the presence of elemental sulfur in a sealed tube, followed by a controlled reduction step. The crystal structure patterns (X-ray diffraction) and optical spectra (UV-IR) of Y2O2SO4, Y2O2S, and crystalline Y2O3 were collected throughout the treatment steps to correlate the structural transformations (via thermogravimetric analysis) with the optical properties. Local and long-range crystallinities were characterized by using X-ray and optical spectroscopy approaches. Systematic shifts in the Eu3+ excitation and emission peaks were observed as a function of SO42- and S2- concentrations resulting from a crystal evolution from cubic (Y2O3) to trigonal (Y2O2S) and monoclinic (Y2O2SO4), which can modify the local hybridization of sensitizer dopants (i.e., Ce3+). Ultimately, Tb3+ and Tb3+/Ce3+ doping was employed in these hosts (Y2O2SO4, Y2O2S, and Y2O3) to understand energy transfer between sensitizer and activator ions, which showed significant enhancement for the monoclinic sulfate structure.

Salt Effects on the Phase Behavior and Cocrystallization Kinetics of POCB-Water Mixtures

Mixtures of water with polyoxacyclobutane (POCB) have a unique phase diagram which combines liquid-liquid equilibrium (LLE) at high temperatures and cocrystallization of a POCB-hydrate at low temperatures. Such cocrystal hydrate formation is extremely rare among polymers. We report on the effects of adding NaCl salt on the phase behavior of POCB-water mixtures and the kinetics of hydrate crystallization from such mixtures. Salt loadings of less than 0.1 wt % were found to greatly expand the LLE region. Salt loadings of ∼10 wt % were found to significantly decrease the melting temperature of the hydrate below its ∼37 °C value under salt-free conditions. The hydrate was found to be remarkably tolerant of salt and persists at room temperature even when equilibrated with salt-saturated water. Salt was found to slow down hydrate crystallization, and the degree of slowing was greater than that expected from the salt-induced decrease in undercooling due to melting point depression.

Photocarrier Recombination Dynamics in Highly Scattering Cu2O Nanocatalyst Clusters

Inversion analysis of transient absorption data to capture the photoexcited charge carrier population rate dynamics is a powerful technique for extracting realistic lifetimes and identifying recombination pathways. However, for highly scattering samples such as Cu2O nanoparticles (NPs) with associated dielectric Mie scattering, the scattering leads to an inaccurate measure of the excited photocarrier. This work studies methods to correct for the scattering to generalize the use of inversion analysis and provide secondary information about the nature of the scattering NPs. Scattering profiles of semitransparent disks containing Cu2O NPs with different shapes and sizes are measured to demonstrate that the inclusion of scattering in analysis reduces the photoexcited carrier density by 1 order of magnitude. It is found that the photocarrier density response is affected by shape rather than size. A Fourier transform of the scattering profiles produces a distribution of length scales within the sample characteristic of the mean separation of scatterers. This analysis reveals that NPs are forming clusters. Links are made between the scattering and carrier dynamics.

Unlocking Efficient Ultrafast Bound-Electron Optical Nonlinearities via Mirror Induced Quasi Bound States in the Continuum

The operation of photonic devices often relies on modulation of their refractive index. While the sub-bandgap index change through bound-electron optical nonlinearity offers a faster response than utilizing free carriers with an overbandgap pump, optical switching often suffers from inefficiency. Here, we use a recently observed metasurface based on mirror-induced optical bound states in the continuum, to enable superior modulation characteristics. We achieve a pulsewidth-limited switching time of 100 fs, reflectance change of 22%, remarkably low energy consumption of 255 μJ/cm2, and an enhancement of modulation contrast by a factor of 440 compared to unpatterned silicon. Additionally, the narrow photonic resonance facilitates the detection of the dispersive nondegenerate two-photon nonlinearity, allowing tunable pump and probe excitation. These findings are explained by a two-band theoretical model for the dispersive nonlinear index. The demonstrated efficient and rapid switching holds immense potential for applications, including quantum photonics, sensing, and metrology.

Observation of Near-Field Thermal Radiation between Coplanar Nanodevices with Subwavelength Dimensions

With the continuous advancement of nanotechnology, nanodevices have become crucial components in computing, sensing, and energy conversion applications. The structures of nanodevices typically possess subwavelength dimensions and separations, which pose significant challenges for understanding energy transport phenomena in nanodevices. Here, on the basis of a judiciously designed thermal photonic nanodevice, we report the first measurement of near-field energy transport between two coplanar subwavelength structures over temperature bias up to ∼190 K. Our experimental results demonstrate a 20-fold enhancement in energy transfer beyond blackbody radiation. In contrast with the well-established near-field interactions between two semi-infinite bodies, the subwavelength confinements in nanodevices lead to increased polariton scattering and reduction of supporting photonic modes and, therefore, a lower energy flow at a given separation. Our work unveils exciting opportunities for the rational design of nanodevices, particularly for coplanar near-field energy transport, with important implications for the development of efficient nanodevices for energy harvesting and thermal management.

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

Predicting Leg Forces and Knee Moments Using Inertial Measurement Units: An In Vitro Study

We compared the ability of seven machine learning algorithms to use wearable inertial measurement unit (IMU) data to identify the severe knee loading cycles known to induce microdamage associated with anterior cruciate ligament rupture. Sixteen cadaveric knee specimens, dissected free of skin and muscle, were mounted in a rig simulating standardized jump landings. One IMU was located above and the other below the knee, the applied three-dimensional action and reaction loads were measured via six-axis load cells, and the three-dimensional knee kinematics were also recorded by a laboratory motion capture system. Machine learning algorithms were used to predict the knee moments and the tibial and femur vertical forces; 13 knees were utilized for training each model, while three were used for testing its accuracy (i.e., normalized root-mean-square error) and reliability (Bland-Altman limits of agreement). The results showed the models predicted force and knee moment values with acceptable levels of error and, although several models exhibited some form of bias, acceptable reliability. Further research will be needed to determine whether these types of models can be modified to attenuate the inevitable in vivo soft tissue motion artifact associated with highly dynamic activities like jump landings.

Targeted Delivery of Sucrose-Coated Nanocarriers with Chemical Cargoes to the Plant Vasculature Enhances Long-Distance Translocation

Current practices for delivering agrochemicals are inefficient, with only a fraction reaching the intended targets in plants. The surfaces of nanocarriers are functionalized with sucrose, enabling rapid and efficient foliar delivery into the plant phloem, a vascular tissue that transports sugars, signaling molecules, and agrochemicals through the whole plant. The chemical affinity of sucrose molecules to sugar membrane transporters on the phloem cells enhances the uptake of sucrose-coated quantum dots (sucQD) and biocompatible carbon dots with β-cyclodextrin molecular baskets (suc-β-CD) that can carry a wide range of agrochemicals. The QD and CD fluorescence emission properties allowed detection and monitoring of rapid translocation (<40 min) in the vasculature of wheat leaves by confocal and epifluorescence microscopy. The suc-β-CDs more than doubled the delivery of chemical cargoes into the leaf vascular tissue. Inductively coupled plasma mass spectrometry (ICP-MS) analysis showed that the fraction of sucQDs loaded into the phloem and transported to roots is over 6.8 times higher than unmodified QDs. The sucrose coating of nanoparticles approach enables unprecedented targeted delivery to roots with ≈70% of phloem-loaded nanoparticles delivered to roots. The use of plant biorecognition molecules mediated delivery provides an efficient approach for guiding nanocarriers containing agrochemicals to the plant vasculature and whole plants.

Predicting potential SARS-CoV-2 mutations of concern via full quantum mechanical modelling

Ab initio quantum mechanical models can characterize and predict intermolecular binding, but only recently have models including more than a few hundred atoms gained traction. Here, we simulate the electronic structure for approximately 13 000 atoms to predict and characterize binding of SARS-CoV-2 spike variants to the human ACE2 (hACE2) receptor using the quantum mechanics complexity reduction (QM-CR) approach. We compare four spike variants in our analysis: Wuhan, Omicron, and two Omicron-based variants. To assess binding, we mechanistically characterize the energetic contribution of each amino acid involved, and predict the effect of select single amino acid mutations. We validate our computational predictions experimentally by comparing the efficacy of spike variants binding to cells expressing hACE2. At the time we performed our simulations (December 2021), the mutation A484K which our model predicted to be highly beneficial to ACE2 binding had not been identified in epidemiological surveys; only recently (August 2023) has it appeared in variant BA.2.86. We argue that our computational model, QM-CR, can identify mutations critical for intermolecular interactions and inform the engineering of high-specificity interactors.

E(n) Equivariant Graph Neural Network for Learning Interactional Properties of Molecules

We have developed a multi-input E(n) equivariant graph convolution-based model designed for the prediction of chemical properties that result from the interaction of heterogeneous molecular structures. By incorporating spatial features and constraining the functions learned from these features to be equivariant to E(n) symmetries, the interactional-equivariant graph neural network (IEGNN) can efficiently learn from the 3D structure of multiple molecules. To verify the IEGNN's capability to learn interactional properties, we tested the model's performance on three molecular data sets, two of which are curated in this study and made publicly available for future interactional model benchmarking. To enable the loading of these data sets, an open-source data structure based on the PyTorch Geometric library for batch loading multigraph data points is also created. Finally, the IEGNN's performance on a data set consisting of an unknown interactional relationship (the frictional properties resulting between monolayers with variable composition) is examined. The IEGNN model developed was found to have the lowest mean absolute percent error for the predicted tribological properties of four of the six data sets when compared to previous methods.

'Dark' CO2 fixation in succinate fermentations enabled by direct CO2 delivery via hollow fiber membrane carbonation

Anaerobic succinate fermentations can achieve high-titer, high-yield performance while fixing CO2 through the reductive branch of the tricarboxylic acid cycle. To provide the needed CO2, conventional media is supplemented with significant (up to 60 g/L) bicarbonate (HCO3-), and/or carbonate (CO32-) salts. However, producing these salts from CO2 and natural ores is thermodynamically unfavorable and, thus, energetically costly, which reduces the overall sustainability of the process. Here, a series of composite hollow fiber membranes (HFMs) were first fabricated, after which comprehensive CO2 mass transfer measurements were performed under cell-free conditions using a novel, constant-pH method. Lumen pressure and total HFM surface area were found to be linearly correlated with the flux and volumetric rate of CO2 delivery, respectively. Novel HFM bioreactors were then constructed and used to comprehensively investigate the effects of modulating the CO2 delivery rate on succinate fermentations by engineered Escherichia coli. Through appropriate tuning of the design and operating conditions, it was ultimately possible to produce up to 64.5 g/L succinate at a glucose yield of 0.68 g/g; performance approaching that of control fermentations with directly added HCO3-/CO32- salts and on par with prior studies. HFMs were further found to demonstrate a high potential for repeated reuse. Overall, HFM-based CO2 delivery represents a viable alternative to the addition of HCO3-/CO32- salts to succinate fermentations, and likely other 'dark' CO2-fixing fermentations.

Sex difference in brain functional connectivity of hippocampus in Alzheimer's disease

Alzheimer's disease (AD), affecting nearly 6.5 million people, is the fifth leading cause of death in individuals 65 years or older in the USA. Prior research has shown that AD disproportionality affects females; females have a greater incidence rate, perform worse on a variety of neuropsychological tasks, and have greater total brain atrophy. Recent research has linked these sex differences to neuroimaging markers of brain pathology, such as hippocampal volumes. Specifically, research from our lab found that functional connectivity from the hippocampus to the precuneus cortex and brain stem was significantly stronger in males than in females with mild cognitive impairment. The aim of this study was to extend our understanding to individuals with AD and to determine if these potential sex-specific functional connectivity biomarkers extend through different disease stages. The resting state fMRI and T2 MRI of cognitively normal individuals (n = 32, female = 16) and individuals with AD (n = 32, female = 16) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) were analyzed using the Functional Connectivity Toolbox (CONN). Our results demonstrate that males had a significantly stronger interhemispheric functional connectivity between the left and right hippocampus compared to females. These results improve our current understanding of the role of the hippocampus in sex differences in AD. Understanding the contribution of impaired functional connectivity sex differences may aid in the development of sex-specific precision medicine for improved AD treatment.

Extraordinary Thermal Stability and Sinter Resistance of Sub-2 nm Platinum Nanoparticles Anchored to a Carbon Support by Selenium

Nanoparticle sintering has long been a major challenge in developing catalytic systems for use at elevated temperatures. Here we report an in situ electron microscopy study of the extraordinary sinter resistance of a catalytic system comprised of sub-2 nm Pt nanoparticles on a Se-decorated carbon support. When heated to 700 °C, the average size of the Pt nanoparticles only increased from 1.6 to 2.2 nm, while the crystal structure, together with the {111} and {100} facets, of the Pt nanoparticles was well retained. Our electron microscopy analyses suggested that the superior resistance against sintering originated from the Pt-Se interaction. Confirmed by energy-dispersive X-ray elemental mapping and electron energy loss spectra, the Se atoms surrounding the Pt nanoparticles could survive the heating. This work not only offers an understanding of the physics behind the thermal behavior of this catalytic material but also sheds light on the future development of sinter-resistant catalytic systems.

Closing Dichloramine Decomposition Nitrogen and Oxygen Mass Balances: Relative Importance of End-Products from the Reactive Nitrogen Species Pathway

In drinking water chloramination, monochloramine autodecomposition occurs in the presence of excess free ammonia through dichloramine, the decay of which was implicated in N-nitrosodimethylamine (NDMA) formation by (i) dichloramine hydrolysis to nitroxyl which reacts with itself to nitrous oxide (N2O), (ii) nitroxyl reaction with dissolved oxygen (DO) to peroxynitrite or mono/dichloramine to nitrogen gas (N2), and (iii) peroxynitrite reaction with total dimethylamine (TOTDMA) to NDMA or decomposition to nitrite/nitrate. Here, the yields of nitrogen and oxygen-containing end-products were quantified at pH 9 from NHCl2 decomposition at 200, 400, or 800 μeq Cl2·L-1 with and without 10 μM-N TOTDMA under ambient DO (∼500 μM-O) and, to limit peroxynitrite formation, low DO (≤40 μM-O). Without TOTDMA, the sum of free ammonia, monochloramine, dichloramine, N2, N2O, nitrite, and nitrate indicated nitrogen recoveries ±95% confidence intervals were not significantly different under ambient (90 ± 6%) and low (93 ± 7%) DO. With TOTDMA, nitrogen recoveries were less under ambient (82 ± 5%) than low (97 ± 7%) DO. Oxygen recoveries under ambient DO were 88-97%, and the so-called unidentified product of dichloramine decomposition formed at about three-fold greater concentration under ambient compared to low DO, like NDMA, consistent with a DO limitation. Unidentified product formation stemmed from peroxynitrite decomposition products reacting with mono/dichloramine. For a 2:2:1 nitrogen/oxygen/chlorine atom ratio and its estimated molar absorptivity, unidentified product inclusion with uncertainty may close oxygen recoveries and increase nitrogen recoveries to 98% (ambient DO) and 100% (low DO).

Rh@Au Core-Shell Nanocrystals with the Core in Tensile Strain and the Shell in Compressive Strain

Bimetallic nanocrystals provide a versatile platform for utilizing the desired characteristics of two different elements within one particle. Core-shell nanocrystals, in particular, have found widespread use in catalysis by providing an ability to leverage the strains arising from the lattice mismatch between the core and the shell. However, large (>5%) lattice mismatch tends to result in nonepitaxial growth and lattice defects in an effort to release the strain. Herein, we report the epitaxial growth of Au on Rh cubic seeds under mild reaction conditions to generate Rh@Au truncated octahedra featuring a lattice mismatch of 7.2%. Key to the success was the use of small (4.5 nm) Rh cubes as seeds, which could homogeneously distribute the tensile strain arising from the epitaxial growth of a conformal, compressively strained Au shell. Further, delicate tuning of kinetic parameters through the introduction of NaOH and KBr into the synthesis allowed for a unique nucleation pattern that led to centrally located cores and a narrow size distribution for the product. A thorough investigation of the various possible highly strained morphologies was conducted to gain a full understanding of the system.

Dealkylation of Poly(methyl methacrylate) by TiCl4 Vapor Phase Infiltration (VPI) and the Resulting Chemical and Thermophysical Properties of the Hybrid Material

This study examines the chemical reaction pathways for vapor phase infiltration (VPI) of TiCl4 into poly(methyl methacrylate) (PMMA). VPI is a processing method that transforms organic polymers into organic-inorganic hybrid materials with new properties of interest for microelectronic patterning, technical textiles, and chemical separations. Understanding the fundamental chemical mechanisms of the VPI process is essential for establishing approaches to design the chemical structure and properties of these hybrid materials. While prior work has suggested that TiCl4 infiltration into PMMA does not disrupt the polymer's carbonyl bond, a clear reaction mechanism has yet to be proposed. Here, we present a detailed X-ray photoelectron spectroscopy study that presents evidence for a concerted reaction mechanism that involves TiCl4 coordinating with the PMMA's ester group to dealkylate the methyl side group, creating a chloromethane byproduct and primary chemical bonds between the organic and inorganic components of the hybrid material. Additional spectroscopy, quartz crystal microbalance gravimetry, and thermophysical and chemical property measurements of this material, including solubility studies and thermal expansion measurements, provide further evidence for this chemical reaction pathway and the subsequent creation of inorganic cross-links that network these TiOx-PMMA hybrid materials.

Invariant Molecular Representations for Heterogeneous Catalysis

Catalyst screening is a critical step in the discovery and development of heterogeneous catalysts, which are vital for a wide range of chemical processes. In recent years, computational catalyst screening, primarily through density functional theory (DFT), has gained significant attention as a method for identifying promising catalysts. However, the computation of adsorption energies for all likely chemical intermediates present in complex surface chemistries is computationally intensive and costly due to the expensive nature of these calculations and the intrinsic idiosyncrasies of the methods or data sets used. This study introduces a novel machine learning (ML) method to learn adsorption energies from multiple DFT functionals by using invariant molecular representations (IMRs). To do this, we first extract molecular fingerprints for the reaction intermediates and later use a Siamese-neural-network-based training strategy to learn invariant molecular representations or the IMR across all available functionals. Our Siamese network-based representations demonstrate superior performance in predicting adsorption energies compared with other molecular representations. Notably, when considering mean absolute values of adsorption energies as 0.43 eV (PBE-D3), 0.46 eV (BEEF-vdW), 0.81 eV (RPBE), and 0.37 eV (scan+rVV10), our IMR method has achieved the lowest mean absolute errors (MAEs) of 0.18 0.10, 0.16, and 0.18 eV, respectively. These results emphasize the superior predictive capacity of our Siamese network-based representations. The empirical findings in this study illuminate the efficacy, robustness, and dependability of our proposed ML paradigm in predicting adsorption energies, specifically for propane dehydrogenation on a platinum catalyst surface.