Axial-Stressed Piezoresistive Nanobeam for Ultrahigh Chemomechanical Sensitivity to Molecular Adsorption

This study proposes nanothickness piezoresistive double-clamped beams that are used in a double-side adsorbing mode. The axially stressed clamped beam exhibits continually increasing sensitivity as it is thinned down to nanoscale, and the thinning is theoretically without limitation. Sensing experiments to part-per-million levels of trimethylamine vapor well verify the proposal. A 93 nm thick beam sensor exhibits higher than 1 order of magnitude sensitivity compared to typical piezoresistive cantilever sensors, and its chemomechanical sensing resolution is comparable with that obtained by the off-cantilever optical detection method. With the nanobeam, a surprisingly ultrahigh sensitivity to surface molecular self-assembly induced surface stress is also obtained that is about 150 times higher than that obtained from a conventional cantilever. With additional advantages of elimination of single-sided adsorption induced bimetallic effect noise, tinier size, and easier fabrication, the ultrasensitive nanothick beam sensors show promise to replace the state-of-the-art piezoresistive cantilevers for bio/chemical nanomechanical detection

Regioselective Patterning of Multiple SAMs and Applications in Surface-Guided Smart Microfluidics

A top-down nanofabrication technology is developed to integrate multiple SAMs (self-assembled monolayers) into regioselective patterns. With ultraviolet light exposure through regioselectively hollowed hard mask, an existing SAM at designated microregions can be removed and a dissimilar kind of SAM can be regrown there. By repeating the photolithography-like process cycle, diverse kinds of SAM building blocks can be laid out as a desired pattern in one microfluidic channel. In order to ensure high quality of the surface modifications, the SAMs are vapor-phase deposited before the channel is closed by a bonding process. For the first time the technique makes it possible to integrate three or more kinds of SAMs in one microchannel. The technique is very useful for multiplex surface functionalization of microfluidic chips where different segments of a microfluidic channel need to be individually modified with different SAMs or into arrayed pattern for surface-guided fluidic properties like hydrophobicity/philicity and/or oleophobicity/philicity, etc. The technique has been well validated by experimental demonstration of various surface-directed flow-guiding functions. By modifying a microchannel surface into an arrayed pattern of multi-SAM “two-tone” stripe array, surface-guiding-induced 3D swirling flow is generated in a microfluidic channel that experimentally exhibits quick oil/water mixing and high-efficiency oil-to-water chemical extraction

Functionalized Mesoporous Silica for Microgravimetric Sensing of Trace Chemical Vapors

Featuring a huge surface-to-volume ratio, synthesized SBA-15 mesoporous silica is functionalized by inner-channel-wall modification of sensing groups for highly specific chemical-vapor detection at trace level. With the developed sensing material loaded on resonant microcantilevers, the specifically adsorbed chemical-vapor molecules act as an added mass to shift the cantilever resonant frequency for gravimetric sensing signal readout. Two kinds of sensing materials for trinitrotoluene (TNT) and ammonia/amine are respectively prepared by inner-wall layer-by-layer grafting functionalization. By using hexafluoro-2-propanol-functionalized mesoporous silica (HFMS), experimental results show highly specific and rapid detection of TNT vapor, with a ppt-level detection limit; functionalized with a carboxyl (COOH) group, the mesoporous silica is loaded onto the cantilever resonating sensor that experimentally exhibits an ultrafine detection limit of tens of ppb to ammonia/amine gases

Microgravimetric Analysis Method for Activation-Energy Extraction from Trace-Amount Molecule Adsorption

Activation-energy (<i>E</i>_a) value for trace-amount adsorption of gas molecules on material is rapidly and inexpensively obtained, for the first time, from a microgravimetric analysis experiment. With the material loaded, a resonant microcantilever is used to record in real time the adsorption process at two temperatures. The kinetic parameter <i>E</i>_a is thereby extracted by solving the Arrhenius equation. As an example, two CO₂ capture nanomaterials are examined by the <i>E</i>_a extracting method for evaluation/optimization and, thereby, demonstrating the applicability of the microgravimetric analysis method. The achievement helps to solve the absence in rapid quantitative characterization of sorption kinetics and opens a new route to investigate molecule adsorption processes and materials

Additional file 1 of The serum lipid profiles in immune thrombocytopenia: Mendelian randomization analysis and a retrospective study

Supplementary Material

Palladium-Catalyzed Tandem Hydrocarbonylative Lactamization and Cycloaddition Reaction for the Construction of Bridged Polycyclic Lactams

The intramolecular hydroaminocarbonylation of alkenes is a compelling tool to rapidly access lactam, a privileged motif ubiquitous in natural products, pharmaceuticals, and agrochemicals. However, selective carbonylation to bridged polycyclic lactams with a lactam nitrogen at a bridgehead position is less explored. We herein report a modular palladium-catalyzed approach to perform a tandem hydrocarbonylative lactamization/Diels–Alder cycloaddition reaction with 2-vinyl aryl aldimines, alkenes, and CO, which offers convenient access to furnish the bridged polycyclic lactams in high yields with high selectivities

Microgravimetric Thermodynamic Modeling for Optimization of Chemical Sensing Nanomaterials

On the basis of microgravimetric sensing data, an analytical modeling method is proposed for comprehensive evaluation and optimization of gas sensing or adsorbing related functional materials. Resonant microcantilever is loaded with the material to be evaluated for a gravimetric sensing experiment. With sensing isotherm curves obtained at different temperatures, key thermodynamic and kinetic parameters of the material, such as enthalpy Δ<i><i>H</i>°</i>, Gibbs free energy, adsorption rate constant <i>K</i>_<i>a</i>, and coverage θ, etc., can be quantitatively extracted for optimal selection and design. On the basis of the gravimetric experiment, the modeling method is used on three sorts of trimethylamine sensing nanomaterials of mesoporous silica nanoparticles (MSNs). The COOH-functionalized material is clearly identified as the best sensing material among the three similar ones, thereby validating high accuracy of the proposed model. Broad applicability of the modeling method to other sensing materials and/or target gases is also experimentally confirmed, where sensing properties of a functionalized hyper-branched polymer to organophorous simulant of dimethyl methylphosphonate (DMMP) are still evaluated well. In addition to sensing materials, the gravimetric experiment-based modeling method can be expanded to other functional materials like moisture absorbents or detoxification agents. Water adsorbing experiment on KIT-5 mesoporous-silica is modeled, with the low −Δ<i><i>H</i>°</i> value (i.e., low adsorption heat) result, indicating that the KIT-5 is a good adsorbent to humidity. Alternatively, the modeled high −Δ<i><i>H</i>°</i> value (i.e., high reaction heat) shows promising usage of SBA-15 mesoporous-silica as detoxification material to hazardous organophorous chemicals. Therefore, the analytical modeling technology can be used for developing and evaluating new adsorbing materials for gas sensing, fixing, and detoxification applications

Machine Learning Combined with Weighted Voting Regression and Proactive Searching Progress to Discover ABO_3‑δ Perovskites with High Oxide Ionic Conductivity

ABO3‑δ-type perovskites are one of the important oxygen ion conductors because of the enhanced properties through adjustments to the composition via elemental doping. In this work, machine learning combined with weighted voting regression (WVR) and proactive searching progress (PSP) was used to develop a model with high accuracy for the prediction of the oxide ionic conductivity of doped ABO3‑δ perovskites. After feature selection, algorithm selection, and parameter optimization, Gradient Boosting regression (GBR), random forest regression (RFR), and extra trees regression (ETR) were determined to be the optimal methods for WVR in constructing the integrated model. The R values of leave-one-out cross-validation (LOOCV) and the test set for the integrated model MWVR could reach 0.812 and 0.920, respectively. After the PSP was conducted, a total of 179 perovskites with high oxide ionic conductivity were discovered. PSP searching identified 8 types of perovskites with high oxide ionic conductivity. Pattern recognition was employed to identify the optimization area that exhibited a high oxide ionic conductivity. Visualization of factor effects was used to visualize the effect of the doping element type and ratio on the oxide ionic conductivity. The Shapley Additive exPlanations (SHAP) analysis of the significant features revealed that Ra/Rb had the highest influence on the oxide ionic conductivity with a negative impact. The developed integrated model, explored patterns, and optimization areas in this work can serve as a valuable guide for the discovery and design of perovskites with high oxide ionic conductivity

Table_1_Ghrelin Ameliorates Traumatic Brain Injury by Down-Regulating bFGF and FGF-BP.DOCX

Traumatic brain injury (TBI) is a primary cause of disability and mortality. Ghrelin, a gastrointestinal hormone, has been found to have protective effects for the brain, but the molecular mechanism of these neuroprotective effects of ghrelin remains unclear. In this study, an electronic cortical contusion impactor was used to establish a rat TBI model and we investigated the effect of ghrelin on brain repair by neurological severity score and histological examination. An antibody array was employed to uncover the molecular mechanism of ghrelin’s neuroprotective effects by determining the alterations of multiple proteins in the brain cortex. As a result, ghrelin attenuated brain injury and promoted brain functional recovery. After TBI, 13 proteins were up-regulated in the brain cortex, while basic fibroblast growth factor (bFGF) and fibroblast growth factor-binding protein (FGF-BP) were down-regulated after ghrelin treatment. It is known that bFGF can induce angiogenesis in the brain and accelerate wound healing, which can be further enhanced by FGF-BP. Based on the previous studies, it is hypothesized that the exogenous ghrelin curing TBI might cause the closure of bFGF and FGF-BP functions on wound healing, or ghrelin might exert the neuroprotective effects by competitively inhibiting bFGF/FGF-BP-induced neovascularization. Whether the combinational administration of ghrelin and bFGF/FGF-BP can enhance or weaken the therapeutic effect on TBI requires further research.</p

Search for ABO₃ Type Ferroelectric Perovskites with Targeted Multi-Properties by Machine Learning Strategies

Ferroelectric perovskites are one of the most promising functional materials due to the pyroelectric and piezoelectric effect. In the practical applications of ferroelectric perovskites, it is often necessary to meet the requirements of multiple properties. In this work, a multiproperties machine learning strategy was proposed to accelerate the discovery and design of new ferroelectric ABO3-type perovskites. First, a classification model was constructed with data collected from publications to distinguish ferroelectric and nonferroelectric perovskites. The classification accuracies of LOOCV and the test set are 87.29% and 86.21%, respectively. Then, two machine learning strategies, Machine-Learning Workflow and SISSO, were used to construct the regression models to predict the specific surface area (SSA), band gap (Eg), Curie temperature (Tc), and dielectric loss (tan δ) of ABO3-type perovskites. The correlation coefficients of LOOCV in the optimal models for SSA, Eg, and Tc are 0.935, 0.891, and 0.971, respectively, while the correlation coefficient of the predicted and experimental values of the SISSO model for tan δ prediction could reach 0.913. On the basis of the models, 20 ABO3 ferroelectric perovskites with three different application prospects were screened out with the required properties, which could be explained by the patterns between the important descriptors and the properties by using SHAP. Furthermore, the constructed models were developed into web servers for the researchers to accelerate the rational design and discovery of ABO3 ferroelectric perovskites with desired multiple properties