Continuous Polarizability-Based Separation of Lithium Iron Phosphate and Graphite Using a Dielectrophoretic Particle Separator.

The recovery of valuable materials from spent lithium-ion batteries (LIBs) has experienced increasing demand in recent years. Current recycling technologies are typically energy-intensive and are often plagued by high operation costs, low processing efficiency, and environmental pollution concerns. In this study, an efficient and environmentally friendly dielectrophoresis (DEP)-based approach is proposed to separate the main components of "black mass" mixtures from LIBs, specifically lithium iron phosphate (LFP) and graphite, based on their polarizability differences.

Steroids and steroid-like compounds alter the ion permeability of phospholipid bilayers distinct interactions with lipids and interfacial water.

Steroids are organic compounds found in all forms of biological life. Besides their structural roles in cell membranes, steroids act as signalling molecules in various physiological processes and are used to treat inflammatory conditions. It has been hypothesised that in addition to their well-characterised genomic and non-genomic pathways, steroids exert their biological or pharmacological activities an indirect, nonreceptor-mediated membrane mechanism caused by steroid-induced changes to the physicochemical properties of cell membranes.

Horizontal Distortion Correction of AFM Images Based on Automatic Labeling of Feature Graphics.

The atomic force microscope (AFM) image will be inclined and bent due to the tilt angle between the probe and the sample surface. When the least squares fitting method is used to correct the horizontal distortion of the AFM image, the shape structure that is lower or higher than the sample base will affect the final fitting correction result. In view of the limitations of existing methods and the diversity of AFM images, an AFM image level distortion correction method based on automatic feature marking is proposed.

Insulator-donor electron wavefunction coupling in pseudo-bilayer organic solar cells achieving a certificated efficiency of 19.18.

The incorporation of polymeric insulators has led to notable achievements in the field of organic semiconductors. By altering the blending concentration, polymeric insulators exhibit extensive capabilities in regulating molecular configuration, film crystallinity, and mitigation of defect states. However, current research suggests that the improvement in such physical properties is primarily attributed to the enhancement of thin film morphology, an outcome that seems to be an inevitable consequence of incorporating insulators.

Unlocking new possibilities in ionic thermoelectric materials: a machine learning perspective.

The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.

Optic nerve-mediated modulation of temporally interfering electric fields for potential targeted retinal disease therapy: a computational modeling study.

Introduction: Traditional extraocular electrical stimulation typically produces diffuse electric fields across the retina, limiting the precision of targeted therapy. Temporally interfering (TI) electrical stimulation, an emerging approach, can generate convergent electric fields, providing advantages for targeted treatment of various eye conditions.

Objective: Understanding how detailed structures of the retina, especially the optic nerve, affects electric fields can enhance the application of TI approach in retinal neurodegenerative and vascular diseases, an essential aspect that has been frequently neglected in previous researches.

Comparison of the effects of transcutaneous electrical nerve stimulation and microcurrent electrical neuromuscular stimulation after total knee arthroplasty.

[Purpose] This study aimed to compare the effects of transcutaneous electrical nerve stimulation and microcurrent electrical neuromuscular stimulation on pain relief and knee function following total knee arthroplasty. [Participants and Methods] This was a prospective, single-center, three-group parallel study. Thirty-five patients scheduled for total knee arthroplasty were divided into transcutaneous electrical nerve stimulation, microcurrent electrical neuromuscular stimulation, and control groups.

Facile development of carbon nanotube (CNT)-based flexible thermoelectric materials for energy-harvesting applications.

In this study, we investigate the thermoelectric properties of functionalized multi-walled carbon nanotubes (F-MWCNTs) dispersed over a flexible substrate through a facile vacuum filtration route. To improve their interfacial adhesion and dispersion, F-MWCNTs underwent hot-pressing. The heat-treatment has improved the nanotubes' connections and subsequently reduced porosity as well, which results in an increasing electrical conductivity upon increasing temperature of hot-pressing.

An optimized support vector machine for lung cancer classification system.

Introduction: Lung cancer is one of the main causes of the rising death rate among the expanding population. For patients with lung cancer to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of thisresearch is to enhance machine learning to increase the precision and quality of lung cancer classification.

In-plane aligned doping pattern in electrospun PEI/MBene nanocomposites for high-temperature capacitive energy storage.

To achieve superior energy storage performance in dielectric polymer films, it is crucial to balance three key properties: high dielectric constant, high breakdown strength, and low dielectric loss. Here, we present the realization of ultrahigh efficiency and energy density in electrospun MBene/PEI composite films, achieved through an in-plane aligned doping pattern. The 1.

Current trends in intraoperative neurophysiological monitoring among Asia-Pacific countries: an Asia-Pacific Spine Society survey.

Study Design: A prospective web-based survey.

Purpose: Although intraoperative neurophysiological monitoring (IONM) is critical in spine surgery, its usage is largely based on the surgeon's discretion, and studies on its usage trends in Asia-Pacific countries are lacking. This study aimed to examine current trends in IONM usage in Asia-Pacific countries.

Delineating specific structural and functional patterns for neurophysiological biotypes of childhood maltreatment.

Childhood maltreatment (CM) is a major risk factor for numerous mental disorders. The long-term consequences of CM on brain structural and functional plasticity have been well documented. However, the neurophysiological biotypes of CM remain unclear although the childhood trauma questionnaire uses different dimensions to assess trauma types.

Long-Lasting and High-Power-Density Yarn-Based Moisture-Enabled Electric Generator for Self-powered Electronic Textiles.

1D moisture-enabled electric generators (MEGs) hold great promise for powering electronic textiles, but their current limitations in power output and operational duration restrict their application in wearable technology. This study introduces a high-performance yarn-based moisture-enabled electric generator (YMEG), which comprises a carbon-fiber core, a cotton yarn active layer with a radial gradient of poly(4-styrensulfonic acid) and poly(vinyl alcohol) (PSSA/PVA), and an aluminum wire as the outer electrode. The unique design maintains a persistent moisture gradient between the interior and exterior electrodes, enhancing performance through the continuous proton diffusion from PSSA and Al⁺ ions from the aluminum wire.

Joint EANM/EANO/RANO/SNMMI practice guideline/procedure standard for PET imaging of brain metastases: version 1.0.

This joint practice guideline/procedure standard was collaboratively developed by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neuro-Oncology (EANO), and the PET task force of the Response Assessment in Neurooncology Working Group (PET/RANO). Brain metastases are the most common malignant central nervous system (CNS) tumors. PET imaging with radiolabeled amino acids and to lesser extent [F]FDG has gained considerable importance in the assessment of brain metastases, especially for the differential diagnosis between recurrent metastases and treatment-related changes which remains a limitation using conventional MRI.

Artificial intelligence for hemodynamic monitoring with a wearable electrocardiogram monitor.

Background: The ability to non-invasively measure left atrial pressure would facilitate the identification of patients at risk of pulmonary congestion and guide proactive heart failure care. Wearable cardiac monitors, which record single-lead electrocardiogram data, provide information that can be leveraged to infer left atrial pressures.

Methods: We developed a deep neural network using single-lead electrocardiogram data to determine when the left atrial pressure is elevated.

Effect of DLPFC rTMS on anhedonia and alpha asymmetry in depressed patients.

Anhedonia, a core symptom of depression, has been defined as the loss of pleasure or lack of reactivity to pleasurable stimuli. Considering the relevance of alpha asymmetry to MDD and anhedonia, we explored the effect of dorsolateral prefrontal cortex (DLPFC) stimulation on frontal and posterior EEG alpha asymmetry (FAA and PAA, respectively), in this exploratory investigation. 61 participants randomly received sham (n = 11), bilateral (BS; n = 25), or unilateral stimulation (US; n = 25) of the DLPFC.

A lightweight multi scale fusion network for IGBT ultrasonic tomography image segmentation.

The Insulated Gate Bipolar Transistor (IGBT) is a crucial power semiconductor device, and the integrity of its internal structure directly influences both its electrical performance and long-term reliability. However, the precise semantic segmentation of IGBT ultrasonic tomographic images poses several challenges, primarily due to high-density noise interference and visual distortion caused by target warping. To address these challenges, this paper constructs a dedicated IGBT ultrasonic tomography (IUT) dataset using Scanning Acoustic Microscopy (SAM) and proposes a lightweight Multi-Scale Fusion Network (LMFNet) aimed at improving segmentation accuracy and processing efficiency in ultrasonic images analysis.

Machine learning-based estimation of crude oil-nitrogen interfacial tension.

Accurate estimation of interfacial tension (IFT) between nitrogen and crude oil during nitrogen-based gas injection into oil reservoirs is imperative. The previous research works dealing with prediction of IFT of oil and nitrogen systems consider synthetic oil samples such n-alkanes. In this work, we aim to utilize eight machine learning methods of Decision Tree (DT), AdaBoost (AB), Random Forest (RF), K-nearest Neighbors (KNN), Ensemble Learning (EL), Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Multilayer Perceptron Artificial Neural Network (MLP-ANN) to construct data-driven intelligent models to predict crude oil - nitrogen IFT based upon experimental data of real crude oils samples encountered in underground oil reservoirs.

Using electrical resistivity techniques (ERT and SP) for nondestructive detection of seepage channels at the Leitai heritage site, China.

Seepage accelerates the weathering and destruction of cultural heritage sites, posing a major preservation challenge, while the concealed nature of seepage channels complicates their detection due to noninvasive requirements. In this study, we applied a comprehensive geophysical approach, integrating electrical resistivity tomography (ERT) and self-potential (SP) techniques, to image seepage channels within the Leitai heritage site. These potential seepage channels have already caused a collapse pit measuring 3.

Assessment of the potential and application of BeO nanocage for removal of ciprofloxacin from water employing density functional theory.

The modern world is facing the issue of emerging pollutants for its sustainable development. We report a detailed study on the abatement of ciprofloxacin (CIP) by BeO nanocage. Five different geometries of BeO nanocage with CIP i.

Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.

This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements.

Fully distributed energy management strategy for DC bus charging stations with three charging modes.

Although electric vehicles supplied through distributed generators (DGs) have been universally researched to reduce CO emissions, the accurate current sharing regarding islanded multi-bus DC charging stations considering three charging modes of electric vehicles, i.e., constant current mode, constant power mode and constant voltage mode, is rarely realized.

The high value utilization characteristics of coal gangue powder with emulsified asphalt mastic through rheological and viscoelastic damage theory.

In order to solve the problems of rutting and early fatigue cracks in emulsified asphalt cold recycled pavement, and the shortage of natural stone resources and new environmental hazards caused by the use of traditional limestone powder filler. In this study, coal gangue powder was added to prepare Emulsified Asphalt Mastic (EAM) to improve the rheological properties and fatigue performance. A series of tests, including frequency scanning, temperature scanning, Multiple Stress Creep Recovery (MSCR), Linear Amplitude Scanning (LAS), and Fourier Transform Infrared spectroscopy (FTIR) were conducted.

Application of big data technology in enterprise information security management.

This study aims to explore the application value of big data technology (BDT) in enterprise information security (EIS). Its goal is to develop a risk prediction model based on big data analysis to enhance the information security protection capability of enterprises. A big data analysis system that can monitor and intelligently identify potential security risks in real-time is constructed by designing complex network analysis algorithms and machine learning models.

An intelligent incentive-based demand response program for exhaustive environment constrained techno-economic analysis of microgrid system.

The cost-effective scheduling of distributed energy resources through sophisticated optimization algorithms is the main focus of recent work on microgrid energy management. In order to improve load factor and efficiency, load-shifting techniques are frequently used in conjunction with additional complex constraints such as PHEV scheduling and battery life assessment. Pollutant reduction, however, is rarely highlighted as a primary goal.