Correction: Toward robust lithium-sulfur batteries via advancing Li2S deposition.

[This corrects the article DOI: 10.1039/D4SC02420F.].

Toward robust lithium-sulfur batteries via advancing Li2S deposition.

Lithium-sulfur batteries (LSBs) with two typical platforms during discharge are prone to the formation of soluble lithium polysulfides (LiPS), leading to a decrease in the cycling life of the battery. Under practical working conditions, the transformation of S8 into Li2S is cross-executed rather than a stepwise reaction, where the liquid LiPS to solid Li2S conversion can occur at a high state of charge (SOC) to maintain the current requirement. Therefore, advancing Li2S deposition can effectively reduce the accumulation of LiPSs and ultimately improve the reaction kinetics. Herein, a "butterfly material" GeS2-MoS2/rGO is used as a sulfur host. Rich catalytic heterointerfaces can be obtained via the abundant S-S bonds formed between GeS2 and MoS2. MoS2 (left wing) can enhance LiPS adsorption, while the lattice-matching nature of Fdd2 GeS2 (right wing) and Fm3̄m Li2S can induce multiple nucleation and regulate the 3D growth of Li2S. Li2S deposition can be advanced to occur at 80% SOC, thereby effectively inhibiting the accumulation of soluble LiPSs. Attributed to the synergistic effect of catalytic and lattice-matching properties, robust coin and pouch LSBs can be achieved.

Development and inter-laboratory validation of analytical methods for glufosinate and its two metabolites in foods of plant origin.

Glufosinate is widely used to control various weeds. Glufosinate and its main metabolites have become the focus of attention because of their high water solubility and persistence in aquatic systems. Quantification of the agrochemical product and its metabolite residues is essential for the safety of agricultural products. In this study, a highly specific, simple method was developed to directly determine glufosinate and its metabolite residues in 21 plant origin foods by liquid chromatography with tandem mass spectrometry (LC-MS/MS), and it was validated on 11 foods in five laboratories. Finally, the repeatability limit, reproducibility limit, and uncertainty of the method were calculated based on these validated data and used to support the more accurate detection results. Four different chromatographic columns were used to analyze three target compounds, and the anionic polar pesticide column showed the optimum separation and peak shape. Composition of the mobile phase, extraction solvent, and the clean-up procedure were optimized. The developed method was validated on 21 plant origin foods. The average recoveries were 74-115% for all matrices. The validation results of five laboratories showed this method had a good repeatability (RSDr < 9.5%) and reproducibility (RSDR < 18.9%). The method validation parameters met the requirements of guidance established by the European Union (EU) and China for pesticide residue analysis. This methodology can be used for a routine monitoring that performs well for glufosinate and its metabolite residues.

A rapid method for detecting bronopol in fresh fish, shrimp, crab, and shellfish samples using liquid chromatography-tandem mass spectrometry.

Fish farming plays a vital role in providing food, nutrition, and employment globally. However, this industry faces security challenges, necessitating the use of fungicides and preservatives, such as bronopol, to increase product yields. Bronopol (2­bromo-2-nitropropan-1,3-diol; CAS:52-51-7) is widely used in various fields, including food production, cosmetics, and, more recently, aquaculture. Currently, there is a limited number of techniques available for detecting bronopol in aquaculture products. This is primarily due to bronopol's instability, susceptibility to degradation, and tendency to form precipitates that pose challenges in extraction from aquaculture products. For this issue, this study presents a comprehensive method for detecting bronopol content in aquaculture tissues using liquid chromatography-tandem mass spectrometry (LC‒MS/MS). The methodology was optimized, involving extraction with Cu-Zn precipitant, cleanup using a small HLB column, separation on a T3 column, and gradient elution with water and acetonitrile mobile phases. The quantitative approach was employed without the use of an internal standard, following the external standard method. The spiked recoveries at 3 fortification levels (0.1, 0.2, and 1 mg/kg) ranged from 87.1 % to 108.1 % with relative standard deviations RSD ≤ 9.0 %. By applying this method to fresh fish, shrimp, crab, and shellfish samples from a local supermarket, no residues of bronopol were detected, ensuring the reliability of the results. The simplicity, rapidity, and high sensitivity of the method make it a suitable alternative to conventional techniques for bronopol detection. Moreover, the successful validation of the method's recovery and precision supports its potential application in monitoring and preventing the misuse of bronopol in aquaculture, thereby safeguarding aquaculture product quality and protecting public health.

Machine learning-based classification of chronic traumatic brain injury using hybrid diffusion imaging.

Background and purpose: Traumatic brain injury (TBI) can cause progressive neuropathology that leads to chronic impairments, creating a need for biomarkers to detect and monitor this condition to improve outcomes. This study aimed to analyze the ability of data-driven analysis of diffusion tensor imaging (DTI) and neurite orientation dispersion imaging (NODDI) to develop biomarkers to infer symptom severity and determine whether they outperform conventional T1-weighted imaging. Materials and methods: A machine learning-based model was developed using a dataset of hybrid diffusion imaging of patients with chronic traumatic brain injury. We first extracted the useful features from the hybrid diffusion imaging (HYDI) data and then used supervised learning algorithms to classify the outcome of TBI. We developed three models based on DTI, NODDI, and T1-weighted imaging, and we compared the accuracy results across different models. Results: Compared with the conventional T1-weighted imaging-based classification with an accuracy of 51.7-56.8%, our machine learning-based models achieved significantly better results with DTI-based models at 58.7-73.0% accuracy and NODDI with an accuracy of 64.0-72.3%. Conclusion: The machine learning-based feature selection and classification algorithm based on hybrid diffusion features significantly outperform conventional T1-weighted imaging. The results suggest that advanced algorithms can be developed for inferring symptoms of chronic brain injury using feature selection and diffusion-weighted imaging.

Deep Learning Based Walking Tasks Classification in Older Adults Using fNIRS.

Decline in gait features is common in older adults and an indicator of increased risk of disability, morbidity, and mortality. Under dual task walking (DTW) conditions, further degradation in the performance of both the gait and the secondary cognitive task were found in older adults which were significantly correlated to falls history. Cortical control of gait, specifically in the pre-frontal cortex (PFC) as measured by functional near infrared spectroscopy (fNIRS), during DTW in older adults has recently been studied. However, the automatic classification of differences in cognitive activations under single and dual task gait conditions has not been extensively studied yet. In this paper, by considering single task walking (STW) as a lower attentional walking state and DTW as a higher attentional walking state, we aimed to formulate this as an automatic detection of low and high attentional walking states and leverage deep learning methods to perform their classification. We conduct analysis on the data samples which reveals the characteristics on the difference between HbO2 and Hb values that are subsequently used as additional features. We perform feature engineering to formulate the fNIRS features as a 3-channel image and apply various image processing techniques for data augmentation to enhance the performance of deep learning models. Experimental results show that pre-trained deep learning models that are fine-tuned using the collected fNIRS dataset together with gender and cognitive status information can achieve around 81% classification accuracy which is about 10% higher than the traditional machine learning algorithms. We present additional sensitivity metrics such as confusion matrix, precision and F1 score, as well as accuracy on two-way classification between condition pairings. We further performed an extensive ablation study to evaluate factors such as the voxel locations, channels of input images, zero-paddings and pre-training of deep learning model on their contribution or impact to the classification task. Results showed that using pre-trained model, all the voxel locations, and HbO2 - Hb as the third channel of the input image can achieve the best classification accuracy.

Stable Lithium-Sulfur Batteries Ensured by GeS2 and α-S8 Lattice Matching During the Charge Process.

The charge process of lithium-sulfur batteries (LSBs) is a process in which molecular polarity decreases and the volume shrinks gradually, which is the process most likely to cause lithium polysulfides (LiPSs) loss and interfacial collapse. In this work, GeS2 is utilized, whose (111) lattice plane exactly matches with the (113) lattice of α-S8 , to solve these problems. GeS2 can regulate the interconversion-deposition behavior of S-species during the charge process. Soluble LiPSs can be spontaneously adsorbed on the GeS2 surface, then obtain electrons and eventually convert to α-S8 molecules. More importantly, the α-S8 molecules will crystallize uniformly along the (111) lattice plane of GeS2 to maintain a stable cathode-electrolyte interface. Therefore, outstanding charge/discharge LSBs are successfully accomplished.

AMITE: A Novel Polynomial Expansion for Analyzing Neural Network Nonlinearities.

Polynomial expansions are important in the analysis of neural network nonlinearities. They have been applied thereto addressing well-known difficulties in verification, explainability, and security. Existing approaches span classical Taylor and Chebyshev methods, asymptotics, and many numerical approaches. We find that, while these have useful properties individually, such as exact error formulas, adjustable domain, and robustness to undefined derivatives, there are no approaches that provide a consistent method, yielding an expansion with all these properties. To address this, we develop an analytically modified integral transform expansion (AMITE), a novel expansion via integral transforms modified using derived criteria for convergence. We show the general expansion and then demonstrate an application for two popular activation functions: hyperbolic tangent and rectified linear units. Compared with existing expansions (i.e., Chebyshev, Taylor, and numerical) employed to this end, AMITE is the first to provide six previously mutually exclusive desired expansion properties, such as exact formulas for the coefficients and exact expansion errors. We demonstrate the effectiveness of AMITE in two case studies. First, a multivariate polynomial form is efficiently extracted from a single hidden layer black-box multilayer perceptron (MLP) to facilitate equivalence testing from noisy stimulus-response pairs. Second, a variety of feedforward neural network (FFNN) architectures having between three and seven layers are range bounded using Taylor models improved by the AMITE polynomials and error formulas. AMITE presents a new dimension of expansion methods suitable for the analysis/approximation of nonlinearities in neural networks, opening new directions and opportunities for the theoretical analysis and systematic testing of neural networks.

Unraveling the complexities of urban fluvial flood hydraulics through AI.

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myriad of interacting variables. Numerous hydraulic models are available for analyzing urban flooding; however, meeting the demand of high spatial extension and finer discretization and solving the physics-based numerical equations are computationally expensive. Computational efforts increase drastically with an increase in model dimension and resolution, preventing current solutions from fully realizing the data revolution. In this research, we demonstrate the effectiveness of artificial intelligence (AI), in particular, machine learning (ML) methods including the emerging deep learning (DL) to quantify urban flooding considering the lower part of Darby Creek, PA, USA. Training datasets comprise multiple geographic and urban hydraulic features (e.g., coordinates, elevation, water depth, flooded locations, discharge, average slope, and the impervious area within the contributing region, downstream distance from stormwater outfalls and dams). ML Classifiers such as logistic regression (LR), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN) are used to identify the flooded locations. A Deep neural network (DNN)-based regression model is used to quantify the water depth. The values of the evaluation matrices indicate satisfactory performance both for the classifiers and DNN model (F-1 scores- 0.975, 0.991, 0.892, and 0.855 for binary classifiers; root mean squared error- 0.027 for DNN regression). In addition, the blocked K-folds Cross Validation (CV) of ML classifiers in detecting flooded locations showed satisfactory performance with the average accuracy of 0.899, which validates the models to generalize to the unseen area. This approach is a significant step towards resolving the complexities of urban fluvial flooding with a large multi-dimensional dataset in a highly computationally efficient manner.

Nonlinear dynamic analysis of an epidemiological model for COVID-19 including public behavior and government action.

This paper is concerned with nonlinear modeling and analysis of the COVID-19 pandemic currently ravaging the planet. There are two objectives: to arrive at an appropriate model that captures the collected data faithfully and to use that as a basis to explore the nonlinear behavior. We use a nonlinear susceptible, exposed, infectious and removed transmission model with added behavioral and government policy dynamics. We develop a genetic algorithm technique to identify key model parameters employing COVID-19 data from South Korea. Stability, bifurcations and dynamic behavior are analyzed. Parametric analysis reveals conditions for sustained epidemic equilibria to occur. This work points to the value of nonlinear dynamic analysis in pandemic modeling and demonstrates the dramatic influence of social and government behavior on disease dynamics.

Antioxidant, anti-hyperlipidemia and hepatic protection of enzyme-assisted Morehella esculenta polysaccharide.

The aims of this work were to investigate the antioxidant, anti-hyperlipidemia and hepatic protection of Morehella esculenta polysaccharide (MPS) from fruiting body and its enzyme-assisted MPS (EnMPS). The in vitro scavenging rates of EnMPS at 600 mg/L on superoxide, hydroxyl and 1,1­diphenyl­2­pyrazole hydrazide (DPPH) radicals were 76.92 ±â€¯2.61%, 66.74 ±â€¯2.56% and 75.78 ±â€¯2.4%, higher than those of MPS, respectively. Animals experiments showed that the EnMPS exhibited superior abilities of reducing hepatic lipid levels by monitoring the serum enzyme activities (ALP, ALT, ALB and AST) and serum lipid levels (CK, TC, TG, HDL-C, LDL-C and LDH), enhancing the hepatic antioxidant enzymes (FFA, SOD, CAT and T-AOC) and decreasing the lipid peroxidation (MDA and MPO). The results suggested that the EnMPS can act as a natural candidate for developing drugs to reduce blood lipids, resist oxidation and protect the liver.