Vibration reduction of human body biodynamic response in sitting posture under vibration environment by seat backrest support.

Four-degree-of-freedom (4-DOF) human-chair coupling models are constructed to characterize the different contact modes between the head, chest back, waist back and backrest. The seat-to-head transfer ratio (STHT) is used as an evaluation metric for vibration reduction effectiveness. The simulated vibration reduction ratio of the model is close to the experimental results, which proves the validity of the model. The peak STHT is obviously reduced (P < 0.05, T-test) with seat-backrest support. The experiments show that supporting the head ( a 1 , P < 0.05, Wilcoxon matched-pairs signed ranks) has the best vibration reduction effect (21%), supporting the chest back ( a 2 , P < 0.05) has a reduced effect (11%), and supporting the waist back ( a 3 , P < 0.05) has the weakest effect (4%). When the upper torso is in full contact with the backrest, the peak STHT curve and resonance frequency are positively correlated with the contact stiffness of the seat surface and negatively correlated with the contact damping. In order to reduce the seat-to-head transfer ratio, the lowest STHT peak and lowest total energy judgments were proposed as the selection methods for the selection of the contact stiffness and damping of the backrest in two environments (periodic and non-periodic excitation), respectively.

Tailoring Interfaces for Enhanced Methanol Production from Photoelectrochemical CO2 Reduction.

Efficient and stable photoelectrochemical reduction of CO2 into highly reduced liquid fuels remains a formidable challenge, which requires an innovative semiconductor/catalyst interface to tackle. In this study, we introduce a strategy involving the fabrication of a silicon micropillar array structure coated with a superhydrophobic fluorinated carbon layer for the photoelectrochemical conversion of CO2 into methanol. The pillars increase the electrode surface area, improve catalyst loading and adhesion without compromising light absorption, and help confine gaseous intermediates near the catalyst surface. The superhydrophobic coating passivates parasitic side reactions and further enhances local accumulation of reaction intermediates. Upon one-electron reduction of the molecular catalyst, the semiconductor-catalyst interface changes from adaptive to buried junctions, providing a sufficient thermodynamic driving force for CO2 reduction. These structures together create a unique microenvironment for effective reduction of CO2 to methanol, leading to a remarkable Faradaic efficiency reaching 20% together with a partial current density of 3.4 mA cm-2, surpassing the previous record based on planar silicon photoelectrodes by a notable factor of 17. This work demonstrates a new pathway for enhancing photoelectrocatalytic CO2 reduction through meticulous interface and microenvironment tailoring and sets a benchmark for both Faradaic efficiency and current density in solar liquid fuel production.

The Magneto-Mechanical Hyperelastic Property of Isotropic Magnetorheological Elastomers with Hybrid-Size Magnetic Particles.

Isotropic magnetorheological elastomers (MREs) with hybrid-size particles are proposed to tailor the zero-field elastic modulus and the relative magnetorheological rate. The hyperelastic magneto-mechanical property of MREs with hybrid-size CIPs (carbonyl iron particles) was experimentally investigated under large strain, which showed differential hyperelastic mechanical behavior with different hybrid-size ratios. Quasi-static magneto-mechanical compression tests corresponding to MREs with different hybrid size ratios and mass fractions were performed to analyze the effects of hybrid size ratio, magnetic flux density, and CIP mass fraction on the magneto-mechanical properties. An extended Knowles magneto-mechanical hyperelastic model based on magnetic energy, coupling the magnetic interaction, is proposed to predict the influence of mass fraction, hybrid size ratio, and magnetic flux density on the magneto-mechanical properties of isotropic MRE. Comparing the experimental and predicted results, the proposed model can accurately evaluate the quasi-static compressive magneto-mechanical properties, which show that the predicted mean square deviations of the magneto-mechanical constitutive curves for different mass fractions are all in the range of 0.9-1. The results demonstrate that the proposed hyperelastic magneto-mechanical model, evaluating the magneto-mechanical properties of isotropic MREs with hybrid-size CIPs, has a significant stress-strain relationship. The proposed model is important for the characterization of magneto-mechanical properties of MRE-based smart devices.

The Compound Effect of Spatial and Temporal Resolutions on the Accuracy of Urban Flood Simulation.

Flood disaster is one of the critical threats to cities. With the intellectualization tendency of Industry 4.0, refined urban flood models can effectively reproduce flood inundation scenarios and support the decision-making on the response to the flood. However, the spatiotemporal variability of rainfall and the spatial heterogeneity of the surface greatly increase the uncertainties in urban flood simulations. Therefore, it is crucial to account for spatiotemporal variability of rainfall events and grids of the model as accurately as possible to avoid misleading simulation results. This study aims to investigate the effect of temporal resolutions of rainfall and spatial resolutions of the model on urban flood modeling in small urban catchments and to explore a proper combination of spatiotemporal schemes. The IFMS Urban (integrated flood modeling system, urban) is used to construct a one-dimension and two-dimension coupled urban flood model in the typical inundated area in Dongguan, China. Based on five temporal resolutions of rainfall input and four spatial resolutions, the compound effect of spatiotemporal resolutions on the accuracy of urban flood simulations is systematically analyzed, and the variation characteristics are investigated. The results show that the finer the temporal resolution is, the higher the simulation accuracy of the maximum inundated water depth. Considering the spatial resolution, as the spatial grid becomes smaller, the relative error of the maximum inundated water depth decreases, but it also shows some nonlinear characteristics. Therefore, the smaller grid does not always mean a better simulation. The spatial resolution has a greater impact on the flood simulation accuracy than the temporal resolution. The simulation performance reaches the best when the grid interval is 100 m and the rainfall input interval is 5 min, 10 min, or 15 min. Affected by other factors such as terrain slope, the simulation accuracies under different spatiotemporal resolutions present complex nonlinear characteristics. The mechanisms of the compound effect of the spatiotemporal resolutions on the model simulation and the effect of underlying surface and topography on model simulation will be the focus of in-depth exploration for the future urban flood model.

Interpretable tree-based ensemble model for predicting beach water quality.

Tree-based machine learning models based on environmental features offer low-cost and timely solutions for predicting microbial fecal contamination in beach water to inform the public of the health risk. However, many of these models are black boxes that are difficult for humans to understand, which may cause severe consequences such as unexplained decisions and failure in accountability. To develop interpretable predictive models for beach water quality, we evaluate five tree-based models, namely classification tree, random forest, CatBoost, XGBoost, and LightGBM, and employ a state-of-the-art explanation method SHAP to explain the models. When tested on the Escherichia coli (E. coli) concentration data collected from three beach sites along Lake Erie shores, LightGBM, followed by XGBoost, achieves the highest averaged precision and recall scores. For all three sites, both models suggest lake turbidity as the most important predictor, and elucidate the crucial role of accurate local data of wave height and rainfall in the model development. Local SHAP values further reveal the robustness of the importance of lake turbidity as its SHAP value increases nearly monotonically with its value and is minimally affected by other environmental factors. Moreover, we found an intriguing interaction between lake turbidity and day-of-year. This work suggests that the combination of LightGBM and SHAP has a promising potential to develop interpretable models for predicting microbial water quality in freshwater lakes.

cm2-Scale Synthesis of MoTe2 Thin Films with Large Grains and Layer Control.

Owing to the small energy differences between its polymorphs, MoTe2 can access a full spectrum of electronic states from the 2H semiconducting state to the 1T' semimetallic state and from the Td Weyl semimetallic state to the superconducting state in the 1T' and Td phase at low temperature. Thus, it is a model system for phase transformation studies as well as quantum phenomena such as the quantum spin Hall effect and topological superconductivity. Careful studies of MoTe2 and its potential applications require large-area MoTe2 thin films with high crystallinity and thickness control. Here, we present cm2-scale synthesis of 2H-MoTe2 thin films with layer control and large grains that span several microns. Layer control is achieved by controlling the initial thickness of the precursor MoOx thin films, which are deposited on sapphire substrates by atomic layer deposition and subsequently tellurized. Despite the van der Waals epitaxy, the precursor-substrate interface is found to critically determine the uniformity in thickness and grain size of the resulting MoTe2 films: MoTe2 grown on sapphire show uniform films while MoTe2 grown on amorphous SiO2 substrates form islands. This synthesis strategy decouples the layer control from the variabilities of growth conditions for robust growth results and is applicable to growing other transition-metal dichalcogenides with layer control.

Improving the robustness of beach water quality modeling using an ensemble machine learning approach.

Microbial pollution of beach water can expose swimmers to harmful pathogens. Predictive modeling provides an alternative method for beach management that addresses several limitations associated with traditional culture-based methods of assessing water quality. Widely-used machine learning methods often suffer from high variability in performance from one year or beach to another. Therefore, the best machine learning method varies between beaches and years, making method selection difficult. This study proposes an ensemble machine learning approach referred to as model stacking that has a two-layered learning structure, where the outputs of five widely-used individual machine learning models (multiple linear regression, partial least square, sparse partial least square, random forest, and Bayesian network) are taken as input features for another model that produces the final prediction. Applying this approach to three beaches along eastern Lake Erie, New York, USA, we show that generally the model stacking approach was able to generate reliably good predictions compared to all of the five base models. The accuracy rankings of the stacking model consistently stayed 1st or 2nd every year, with yearly-average accuracy of 78%, 81%, and 82.3% at the three studied beaches, respectively. This study highlights the value of the model stacking approach in predicting beach water quality and solving other pressing environmental problems.