Controlled growth of silica-titania hybrid functional nanoparticles through a multistep microfluidic approach

Lung cancer detection in perioperative patients' exhaled breath with nanomechanical sensor array

INTRODUCTION

Breath analysis using a chemical sensor array combined with machine learning algorithms may be applicable for detecting and screening lung cancer. In this study, we examined whether perioperative breath analysis can predict the presence of lung cancer using a Membrane-type Surface stress Sensor (MSS) array and machine learning.

METHODS

Patients who underwent lung cancer surgery at an academic medical center, Japan, between November 2018 and November 2019 were included. Exhaled breaths were collected just before surgery and about one month after surgery, and analyzed using an MSS array. The array had 12 channels with various receptor materials and provided 12 waveforms from a single exhaled breath sample. Boxplots of the perioperative changes in the expiratory waveforms of each channel were generated and Mann-Whitney U test were performed. An optimal lung cancer prediction model was created and validated using machine learning.

RESULTS

Sixty-six patients were enrolled of whom 57 were included in the analysis. Through the comprehensive analysis of the entire dataset, a prototype model for predicting lung cancer was created from the combination of array five channels. The optimal accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 0.809, 0.830, 0.807, 0.806, and 0.812, respectively.

CONCLUSION

Breath analysis with MSS and machine learning with careful control of both samples and measurement conditions provided a lung cancer prediction model, demonstrating its capacity for non-invasive screening of lung cancer.

Recent Advances in Nanomechanical Membrane-Type Surface Stress Sensors towards Artificial Olfaction

Nanomechanical sensors have gained significant attention as powerful tools for detecting, distinguishing, and identifying target analytes, especially odors that are composed of a complex mixture of gaseous molecules. Nanomechanical sensors and their arrays are a promising platform for artificial olfaction in combination with data processing technologies, including machine learning techniques. This paper reviews the background of nanomechanical sensors, especially conventional cantilever-type sensors. Then, we focus on one of the optimized structures for static mode operation, a nanomechanical Membrane-type Surface stress Sensor (MSS), and discuss recent advances in MSS and their applications towards artificial olfaction.

Mild Microfluidic Approaches to Oxide Nanoparticles Synthesis

Oxide nanoparticles (oxide NPs) are advanced materials with a wide variety of applications in different fields. The use of continuous flow methods is particularly appealing for their synthesis due to the high control achieved over the reaction conditions and the easy process scalability. The present review focuses on the preparation of oxide NPs using microfluidic setups at low temperature (≤80 °C), since the employment of mild reaction conditions is crucial for developing sustainable and cost-effective processes. A particular emphasis will be put on the improvement over the final product features (e. g., size, shape, and size distribution) given by flow methods with respect to conventional batch procedures. The main issues that arise by treating NPs suspensions in microfluidic systems are product deposition or channel clogging; mitigation strategies to overcome these drawbacks will also be presented and discussed.

Odor-Based Nanomechanical Discrimination of Fuel Oils Using a Single Type of Designed Nanoparticles with Nonlinear Viscoelasticity

Odors are one of the most diverse and complicated gaseous mixtures so that their discrimination is challenging yet attractive because of the rich information about their origin. The more similar the properties of odors are, the more difficult the discrimination becomes. The practical applications, however, often demand such discrimination, especially with a compact sensing platform. In this paper, we show that a nanomaterial designed for a specific type of odors can clearly discriminate them even with a single nanomechanical sensing channel. Fuel oils and their mixture are used as a model target that has similar chemical properties but different compositions mainly consisting of paraffinic, olefinic, naphthenic, and aromatic hydrocarbons. We demonstrate using octadecyl functionalized silica-titania nanoparticles that the difference in the compositions is successfully picked up based on their high affinity for the aliphatic hydrocarbons and alkyl chain length dependent nonlinear viscoelastic behavior. Such a properly designed material is proved to derive sufficient information from a series of analytes to discriminate them even with a single sensing element. This approach provides a guideline to prepare various sensors whose response properties are distinct and optimized depending on applications.

Determination of quasi-primary odors by endpoint detection

It is known that there are no primary odors that can represent any other odors with their combination. Here, we propose an alternative approach: "quasi" primary odors. This approach comprises the following condition and method: (1) within a collected dataset and (2) by the machine learning-based endpoint detection. The quasi-primary odors are selected from the odors included in a collected odor dataset according to the endpoint score. While it is limited within the given dataset, the combination of such quasi-primary odors with certain ratios can reproduce any other odor in the dataset. To visually demonstrate this approach, the three quasi-primary odors having top three high endpoint scores are assigned to the vertices of a chromaticity triangle with red, green, and blue. Then, the other odors in the dataset are projected onto the chromaticity triangle to have their unique colors. The number of quasi-primary odors is not limited to three but can be set to an arbitrary number. With this approach, one can first find "extreme" odors (i.e., quasi-primary odors) in a given odor dataset, and then, reproduce any other odor in the dataset or even synthesize a new arbitrary odor by combining such quasi-primary odors with certain ratios.

Synthesis and Surface Engineering of Inorganic Nanomaterials Based on Microfluidic Technology

The controlled synthesis and surface engineering of inorganic nanomaterials hold great promise for the design of functional nanoparticles for a variety of applications, such as drug delivery, bioimaging, biosensing, and catalysis. However, owing to the inadequate and unstable mass/heat transfer, conventional bulk synthesis methods often result in the poor uniformity of nanoparticles, in terms of microstructure, morphology, and physicochemical properties. Microfluidic technologies with advantageous features, such as precise fluid control and rapid microscale mixing, have gathered the widespread attention of the research community for the fabrication and engineering of nanomaterials, which effectively overcome the aforementioned shortcomings of conventional bench methods. This review summarizes the latest research progress in the microfluidic fabrication of different types of inorganic nanomaterials, including silica, metal, metal oxides, metal organic frameworks, and quantum dots. In addition, the surface modification strategies of nonporous and porous inorganic nanoparticles based on microfluidic method are also introduced. We also provide the readers with an insight on the red blocks and prospects of microfluidic approaches, for designing the next generation of inorganic nanomaterials.

Hussein H, H. Ragab A, S. Al-Radadi N. Synthesis and Characterization of Silica-Coated Oxyhydroxide Aluminum/Doped Polymer Nanocomposites: A Comparative Study and Its Application as a Sorbent

The present investigation is a comparison study of two nanocomposites: Nano-silica-coated oxyhydroxide aluminum (SiO2-AlOOH; SCB) and nano-silica-coated oxyhydroxide aluminum doped with polyaniline (SiO2-AlOOH-PANI; SBDP). The prepared nanocomposites were evaluated by monitoring the elimination of heavy metal Ni(II) ions from aquatic solutions. The synthesized nanocomposites were analyzed and described by applying scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM), and Fourier transform infrared spectroscopy (FTIR) techniques, as well as Zeta potential distribution. In this study, two adsorbents were applied to investigate their adsorptive capacity to eliminate Ni(II) ions from aqueous solution. The obtained results revealed that SBDP nanocomposite has a higher negative zeta potential value (-47.2 mV) compared with SCB nanocomposite (-39.4 mV). The optimum adsorption was performed at pH 8, with approximately 94% adsorption for SCB and 97% adsorption for SBDP nanocomposites. The kinetics adsorption of Ni ions onto SCB and SBDP nanocomposites was studied by applying the pseudo first-order, pseudo second-order, and Mories-Weber models. The data revealed that the adsorption of Ni ions onto SCB and SBDP nanocomposites followed the pseudo second-order kinetic model. The equilibrium adsorption data were analyzed using three models: Langmuir, Freundlich, and Dubinin-Radusekevisch-Kanager Isotherm. It was concluded that the Langmuir isotherm fits the experimental results well for the SCB and SBDP nanocomposites. Thermodynamic data revealed that the adsorption process using SCB nanocomposites is an endothermic and spontaneous reaction. Meanwhile, the Ni ion sorption on SBDP nanocomposites is exothermic and spontaneous reaction.

Finite Element Analysis of Interface Dependence on Nanomechanical Sensing

Nanomechanical sensors and their arrays have been attracting significant attention for detecting, discriminating and identifying target analytes. The sensing responses can be partially explained by the physical properties of the receptor layers coated on the sensing elements. Analytical solutions of nanomechanical sensing are available for a simple cantilever model including the physical parameters of both a cantilever and a receptor layer. These analytical solutions generally rely on the simple structures, such that the sensing element and the receptor layer are fully attached at their boundary. However, an actual interface in a real system is not always fully attached because of inhomogeneous coatings with low affinity to the sensor surface or partial detachments caused by the exposure to some analytes, especially with high concentration. Here, we study the effects of such macroscopic interfacial structures, including partial attachments/detachments, for static nanomechanical sensing, focusing on a Membrane-type Surface stress Sensor (MSS), through finite element analysis (FEA). We simulate various macroscopic interfacial structures by changing the sizes, numbers and positions of the attachments as well as the elastic properties of receptor layers (e.g., Young’s modulus and Poisson’s ratio) and evaluate the effects on the sensitivity. It is found that specific interfacial structures lead to efficient sensing responses, providing a guideline for designing the coating films as well as optimizing the interfacial structures for higher sensitivity including surface modification of the substrate.

Microfluidics for Production of Particles: Mechanism, Methodology, and Applications

In the past two decades, microfluidics-based particle production is widely applied for multiple biological usages. Compared to conventional bulk methods, microfluidic-assisted particle production shows significant advantages, such as narrower particle size distribution, higher reproducibility, improved encapsulation efficiency, and enhanced scaling-up potency. Herein, an overview of the recent progress of the microfluidics technology for nano-, microparticles or droplet fabrication, and their biological applications is provided. For both nano-, microparticles/droplets, the previously established mechanisms behind particle production via microfluidics and some typical examples during the past five years are discussed. The emerging interdisciplinary technologies based on microfluidics that have produced microparticles or droplets for cellular analysis and artificial cells fabrication are summarized. The potential drawbacks and future perspectives are also briefly discussed.

Facile synthesis of zirconia-coated mesoporous silica particles by hydrothermal strategy under low potential of hydrogen conditions and functionalization with dodecylphosphonic acid for high-performance liquid chromatography

In this work, multilayer zirconia-coated silica (ZrO₂/SiO₂-n) microspheres were successfully produced by a straightforward hydrothermal procedure with a low concentration of Zr⁴⁺ (5 mM) under low potential of hydrogen (pH) conditions (pH = =2). The obtained ZrO₂/SiO₂-n materials exhibited favorable characteristics for high-performance liquid chromatography (HPLC) separation, including high surface area and pore volume, good pore structure, narrow particle size, and pore size distribution. In addition, the zirconia coverage in the mesopores was confirmed by soaking the material in 1 M NaOH solution, with the particles showing strong resistance to the basic solution. The obtained ZrO₂/SiO₂-n stationary phases were packed into a fused-silica capillary tubing for the separation of alkaloids in hydrophilic interaction chromatography (HILIC) mode, and a column efficiency of 47,800 plates/m was obtained for berberine on a ZrO₂/SiO₂-6 micro column. The ZrO₂/SiO₂-6 microspheres were further modified by dodecylphosphonic acid (C₁₂P-2-ZrO₂/SiO₂-6); the C₁₂P-2-ZrO₂/SiO₂-6 material showed great potential for application in reversed-phase liquid chromatography (RPLC) mode. The C₁₂P-2-ZrO₂/SiO₂-6 micro column showed a column efficiency of 55,000 plates/m for naphthalene and 51,300 plates/m for benzene.

Continuous Multistage Synthesis and Functionalization of Sub-100 nm Silica Nanoparticles in 3D-Printed Continuous Stirred-Tank Reactor Cascades

The controlled and continuous production of nanoparticles (NPs) with functionalized surfaces remains a technological challenge. We present a multistage synthetic platform, consisting of 3D-printed miniature continuous stirred-tank reactor (CSTR) cascades, for the continuous synthesis and functionalization of SiO₂ NPs. The use of the CSTR platform provides ideal and rapid mixing of precursor solutions, precise injection of additional reagents for multistep reactions, and facile operation when using viscous solutions and handling of syntheses with longer reaction times. To exemplify the use of such custom-designed CSTR cascades, amine- and carbohydrate-functionalized SiO₂ NPs are chosen as model reaction systems. In particular, the intensified flow reactor units allowed for the reproducible formation of SiO₂ NPs with diameters less than 100 nm and narrow size distributions (3-8%). Most importantly, by assembling various 3D-printed CSTR cascades, we synthesized gluconolactone-capped polyethylenimine-modified silica NPs in a fully continuous manner. The inherent control over NP surface charge, reactor scalability, and the significant shortening of processing times (less than 10 min) compared to batch methodologies (several days) strongly indicate the ability of the reactor technology to accelerate continuous nanomanufacturing. In general, it provides a simple route for the reproducible preparation of functionalized NPs, thus expanding the gamut of flow reactors for material synthesis.

Free-hand gas identification based on transfer function ratios without gas flow control

Gas identification is one of the most important functions of a gas sensor system. To identify gas species from sensing signals without gas flow control such as pumps or mass flow controllers, it is necessary to extract decisive dynamic features from complex sensing signals due to uncontrolled airflow. For that purpose, various analysis methods using system identification techniques have been proposed, whereas a method that is not affected by a gas input pattern has been demanded to enhance the robustness of gas identification. Here we develop a novel gas identification protocol based on a transfer function ratio (TFR) that is intrinsically independent of a gas input pattern. By combining the protocol with MEMS-based sensors-Membrane-type Surface stress Sensors (MSS), we have realized gas identification with a free-hand measurement, in which one can simply hold a small sensor chip near samples. From sensing signals obtained through the free-hand measurement, we have developed highly accurate machine learning models that can identify odors of spices and herbs as well as solvent vapors. Since no bulky gas flow control units are required, this protocol will expand the applicability of gas sensors to portable electronics, leading to practical artificial olfaction.

Microfluidics for silica biomaterials synthesis: opportunities and challenges

The rational design and controllable synthesis of silica nanomaterials bearing unique physicochemical properties is becoming increasingly important for a variety of biomedical applications from imaging to drug delivery. Microfluidics has recently emerged as a promising platform for nanomaterial synthesis, providing precise control over particle size, shape, porosity, and structure compared to conventional batch synthesis approaches. This review summarizes microfluidics approaches for the synthesis of silica materials as well as the design, fabrication and the emerging roles in the development of new classes of functional biomaterials. We highlight the unprecedented opportunities of using microreactors in biomaterial synthesis, and assess the recent progress of continuous and discrete microreactors and the associated biomedical applications of silica materials. Finally, we discuss the challenges arising from the intrinsic properties of microfluidics reactors for inspiring future research in this field.

Functional Nanoparticles-Coated Nanomechanical Sensor Arrays for Machine Learning-Based Quantitative Odor Analysis

A sensing signal obtained by measuring an odor usually contains varied information that reflects an origin of the odor itself, while an effective approach is required to reasonably analyze informative data to derive the desired information. Herein, we demonstrate that quantitative odor analysis was achieved through systematic material design-based nanomechanical sensing combined with machine learning. A ternary mixture consisting of water, ethanol, and methanol was selected as a model system where a target molecule coexists with structurally similar species in a humidified condition. To predict the concentration of each species in the system via the data-driven approach, six types of nanoparticles functionalized with hydroxyl, aminopropyl, phenyl, and/or octadecyl groups were synthesized as a receptor coating of a nanomechanical sensor. Then, a machine learning model based on Gaussian process regression was trained with sensing data sets obtained from the samples with diverse concentrations. As a result, the octadecyl-modified nanoparticles enhanced prediction accuracy for water while the use of both octadecyl and aminopropyl groups was indicated to be a key for a better prediction accuracy for ethanol and methanol. As the prediction accuracy for ethanol and methanol was improved by introducing two additional nanoparticles with finely controlled octadecyl and aminopropyl amount, the feedback obtained by the present machine learning was effectively utilized to optimize material design for better performance. We demonstrate through this study that various information which was extracted from plenty of experimental data sets was successfully combined with our knowledge to produce wisdom for addressing a critical issue in gas phase sensing.

Precise Synthesis of Well-Defined Inorganic-Organic Hybrid Particles

Synthesis of hybrid particles toward precisely designed hierarchical nanoarchitectures is summarized. In order to satisfy the demands for a variety of materials' performances, the selection of materials, composition and synthesis is carefully done. Flow reactors are one of the useful synthetic means to prepare hybrid materials, especially those with hierarchically and precisely designed multi-components hybrid particles, owing to the efficient mixing and heat exchange in the reactor as well as its connectable (both parallel and sequential) feature. In this review article, after the summary of the preparation of hybrids based on oxides and organics through conventional batch reactors, the application of flow reactors to the preparation of various hybrid particles is introduced to highlight the present status and future possibility of the flow reactor synthesis.

Data-driven nanomechanical sensing: specific information extraction from a complex system

Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.

Controllable synthesis of functional nanoparticles by microfluidic platforms for biomedical applications - a review

Nanoparticles have drawn significant attention in biomedicine due to their unique optical, thermal, magnetic and electrical properties which are highly related to their size and morphologies. Recently, microfluidic systems have shown promising potential to modulate critical stages in nanosynthesis, such as nucleation, growth and reaction conditions so that the size, size distribution, morphology, and reproducibility of nanoparticles are optimized in a high throughput manner. In this review, we put an emphasis on a decade of developments of microfluidic systems for engineering nanoparticles in various applications including imaging, biosensing, drug delivery, and theranostic applications.

Dual external field-responsive polyaniline-coated magnetite/silica nanoparticles for smart fluid applications

In this communication, an electromagnetorheological fluid containing Fe₃O₄/SiO₂/PANI nanoparticles is reported to demonstrate its controllable rheological properties under electric and magnetic fields.