Browse Articles

Enhanced supercapacitor performance using self-assembled silver nanowire electrodes

Controllable selenization kinetics via stabilizing Se concentration enables 14.33% CZTSSe solar cells

The early-stage uniformity and stability of selenium (Se) vapor during selenization critically determine the power conversion efficiency (PCE) of Cu2ZnSn(S,Se)4 (CZTSSe) devices. However, traditional large-volume graphite chambers (Ctrl) provide an overly spacious diffusion environment, leading to delayed Se supply and concentration fluctuations that promote void defects and Cu–Zn disorder. To address this, we propose a small-sized graphite box (S-box) that stabilizes Se delivery at the source by confining the diffusion volume, shortening the mass-transport path, and accelerating concentration equilibration. COMSOL Multiphysics simulations verify that this design markedly improves diffusion efficiency and rapidly establishes a stable concentration field; correspondingly, experiments show that S-box films exhibit higher crystallinity, reduced Sn2+ content, a denser bilayer structure with fewer voids, and a significantly lower overall defect density. Low-temperature photoluminescence further indicates that non-radiative recombination associated with VCu and band tail states is effectively suppressed. Accordingly, devices fabricated with the S-box achieve a champion efficiency of 14.33% (certified at 14.07%), establishing “restricting the Se vapor diffusion space” as the key point of selenization optimization.

Impact of temperature and humidity on the structural and biocompatibility of 3D-Printed PLA scaffolds for bone regeneration

Abstract Fused deposition modeling (FDM) is widely used in medical applications and provides a promising, cost-effective, and user-friendly solution to point-of-care environments. However, this on-site production necessitates extreme process reproducibility in ambient conditions. Although such a requirement is necessary, the effects of environmental temperature and relative humidity during fabrication are poorly understood, especially when complex porous structures are considered. In this paper, we systematically investigate the impact of ambient temperature and relative humidity on the structural, mechanical, and biological performance of porous polylactic acid (PLA) scaffolds fabricated via FDM. Cylindrical porous scaffolds (2.5 cm diameter and heights of 5.3 and 10.3 cm) were printed under controlled conditions in ambient temperature (25–40 °C) and relative humidity (30–70%). Their pore structure (size and density), water-holding capacity, compressive hardness, and in-vitro cytocompatibility were investigated. The geometric fidelity and pore morphology of all scaffolds were similar across fabrication conditions, suggesting that ambient conditions did not influence the qualities in macroscopic visual prints. In comparison, the compressive Young’s modulus increased with increasing temperature. A biocompatibility assay showed that variations in relative humidity had minimal effects on the mechanical performance of the scaffolds but affected cell viability and reactive oxygen species (ROS) generation. Conversely, at higher fabrication temperatures, high intracellular ROS activity was observed without affecting the structural integrity of the scaffolds. These findings establish a practical processing window of moderate temperatures (25–35 °C) and low-to-moderate humidity (30–50% RH) that balances mechanical stability with biological compatibility. These insights into environmental dependence can be used to enhance process reliability and repeatability, which are essential for translating scaffolds printed by FDM into low-cost, high-fidelity clinical and point-of-care applications.

Nano-Raman imaging of monolayer MoS2 nanoribbons

Two-dimensional semiconductors are promising candidates for next-generation electronics. However, characterizing these materials at technologically relevant dimensions remains underexplored. Here, we use tip-enhanced Raman spectroscopy (TERS) to map lithographically-patterned monolayer MoS2 nanoribbons down to 50 nm widths. The surface sensitivity of TERS enables direct nanoscale assessment of the MoS2 surface after a vacuum annealing procedure that removes resist residues. Subsequently, we find a consistently strong TERS response across the nanoribbon, indicating good quality MoS2 and a clean interface to the Au substrate. The good spatial resolution of TERS (down to ∼10 nm) uncovers small, 50–100 nm regions of inhomogeneities, likely arising from the growth process with a higher intensity and redshifted 2LA(M) peak. We also find a 0.5 cm−1 redshift of the A1′ mode at nanoribbon edges, consistent with a fixed negative charge. Our study highlights how advanced nanoscale metrology can be leveraged for future devices and fabrication process optimization.

Do you hate or love AI? Take Nature’s poll

Resource-efficient federated machine unlearning via evolutionary synaptic pruning for cloud-based distributed learning systems

Dual pandemic of firearm injury and COVID-19 in Central and Southeastern Ohio: An interrupted time series analysis

Firearm injuries increased as the United States faced the COVID-19 pandemic, a phenomenon some refer to as the “dual pandemic.” This study examines how one regional trauma system fared during the initial months of the dual pandemic and explores potential explanatory mechanisms of the surge of firearm injuries during the COVID-19 period. We used an interrupted time series model to compare quarterly data from 2016−2021 from the COTS (formerly known as the Central Ohio Trauma System) Regional Trauma Registry to examine the number of firearm injuries, mistriage rates of firearm-injured patients, injury intent, and cases with evidence of substance and alcohol abuse. Among 3,881 firearm-injured patients, demographic characteristics did not vary with respect to firearm injury, mistriage, mortality, substance use, or alcohol use. All but seven outcomes showed a significant level shift at the onset of the COVID-19 period, and all outcomes show a significant slope change during the COVID-19 period. All outcomes but one show a significant level shift at the end of the COVID-19 period, and all outcomes show a significant slope change from the conclusion of the COVID-19 period to the end of the study period. Wilcoxon ranked sum test shows no significant difference in the mean length of stay between the COVID-19 period and all other time included in the study period. This Midwest regional trauma system was affected by the dual pandemic of increased firearm injury during the COVID-19 lockdown. Findings highlight the roles of increased substance use and nonaccidental firearm injuries. Performance indicators reveal some evidence of strain within the region as the lockdown period progressed. Further research should identify region-specific causal mechanisms fueling the dual pandemic and compare the effects among urban, suburban, and rural communities.

An ultrahigh stretchable wearable self-powered hydrogel sensor driven by body heat

Flexible wearable hydrogel sensing devices have attracted significant attention in recent years. However, the integration of high stretchability, body heat-driven self-powering capabilities, and effective strain sensing performance into a single hydrogel poses a significant challenge. In this study, a polyacrylamide (PAM)–laponite XLG (clay)–LiCl hydrogel (PCLH) was fabricated by a simple one-pot synthesis technique at room temperature. The hydrogen bonds formed between PAM and clay, combined with the incorporation of LiCl, facilitate clay dispersion and enhance the stretchability of the hydrogel. Furthermore, the migration of Li+ and Cl− driven by temperature gradients allows the hydrogel to exhibit remarkable thermoelectric properties. The PCLH demonstrates ultrahigh stretchability, with an elongation at break of 6610%, and possesses thermoelectric characteristics, featuring a Seebeck coefficient of 15.32 mV K−1. Based on the excellent stretchability and thermoelectric performance of PCLH, we developed a self-powered wearable sensor that harvests electricity from the temperature difference between the human body and the ambient environment. This device, which is driven by body heat, operates in two distinct working modes—resistive and voltage—making it a promising solution for human motion sensing applications.

Optimizing the mechanical performance of adobe bricks reinforced with Vicia faba plant waste and derived biochar using ANN and RSM

Predicting leaf traits in wine grapes with reflectance spectroscopy

Estimating crop trait data is critical for predicting crop responses to environmental change, enabling more informed diagnoses of crop performance and the development of on-farm management strategies. Yet, many traditional methods for quantifying plant traits are time-consuming and resource-intensive, limiting sample sizes and study durations. In response, high-throughput phenotyping—specifically reflectance spectroscopy—has emerged as a key element of plant trait research, enabling rapid estimation of plant traits. However, little is known about whether reflectance spectroscopy can detect within-species variation in resource acquisition and plant-water traits, especially variation that exists among different cultivars or genotypes of the same crop. Using wine grapes ( V. vinifera subsp. vinifera ) as a focal crop, this study aimed to assess the ability of reflectance spectroscopy to quantify intraspecific variation in 12 leaf traits across 12 different cultivars from seven different varieties. We find significant variability in traits across and within cultivars, especially in gas-exchange and hydraulic traits, with cultivars varying along a resource-conservative-to-resource-acquisitive trait axis. Models based on spectral reflectance data were able to differentiate and predict this fine-scale trait variation among cultivars for seven plant traits, with a predictive power range of R 2  = 0.12–0.57. Models predicting leaf chemical (i.e., carbon and nitrogen concentrations), physiological (i.e., maximum rate of light-saturated photosynthesis), and morphological traits (i.e., leaf dry matter content) were more accurate in their predictions, while models predicting leaf water status were less accurate. Our results indicate that reflectance spectroscopy can capture certain dimensions of the fine-scale trait variation that exists within genetically diverse agroecosystems, though spectroscopic estimates of intraspecific variation in leaf water status are less accurate.

FM and double modulation interferometry with sub-nanometer resolution using the iLens technique

We report a modulation technique applicable to optical two-beam interferometers with unequal arm lengths that allows for highly precise, low-noise displacement measurements below the nanometer regime with a compact optical layout. The technique is inspired by frequency modulation spectroscopy and employs an external electro-optic modulator with sinusoidally phase-modulated laser light in the radio frequency (RF) regime (megahertz frequencies). By combining the RF modulation with a secondary mechanical modulation at a lower frequency to a super-heterodyne scheme we achieve continuous sign-resolved tracking of the arm length difference over many wavelengths, and low-frequency laser noise is effectively suppressed. Our setup is based on the compact interference lens technique and can measure displacements of samples with a wide variety of surface qualities. We examined the system performance using two samples, a mirror surface, and a commercial aneroid capsule without specular reflectivity. Stable multi-fringe operation with sub-nanometer resolution is demonstrated; the noise floor reaches 10 pm/Hz at frequencies above 10 Hz in a regular laboratory environment. This work demonstrates a versatile, simple setup capable of sign-resolved, sub-nanometer displacement sensing suitable even for non-ideal surface conditions.

Ebola outbreak is a global health emergency: what happens next

Prevalence of unreported and uncontrolled dyslipidemia in a food insecure population

KT-YOLO: A multi-convolution kernel collaboration model for dense Hu sheep behavior detection

Computer vision has been extensively applied to sheep behavior detection in recent years. However, the dense distribution of Hu sheep poses detection challenges, while imbalanced behavioral categories in datasets affect classification accuracy for detection tasks in intensive farming scenarios, resulting in high misclassification rates. Current models often rely on over-parameterization to achieve satisfactory detection performance, which increases computational burden and limits practical deployment. To address these challenges, this study introduces the Hu Sheep Behavior Dataset (HSBD), specifically designed for intensive farming environments. The dataset comprises 280 images capturing four behaviors across 6,766 Hu sheep: standing, lying, eating, and drinking. Building upon this foundation, we developed the KT-YOLO model, which utilizes a novel Kernel-Team Fusion (KTF) method to enhance the YOLOv8n detection framework. By employing four different convolution kernel sizes, this method effectively captures multi-scale features and addresses Hu sheep occlusion challenges. To mitigate accuracy degradation caused by dataset imbalance, KT-YOLO incorporates a SlideLoss function during classification, effectively addressing this challenge. Comparative experiments demonstrate that KT-YOLO achieved a mean Average Precision (mAP50) of 86.4%, representing a 6.3 percentage point improvement over YOLOv8n, with SlideLoss contributing an additional 1 percentage point improvement. Further comparison with YOLOv13n demonstrates KT-YOLO’s superior performance in dense Hu sheep behavior detection. By introducing HSBD and developing the innovative KT-YOLO, this study significantly enhances both accuracy and efficiency of dense Hu sheep behavior detection, demonstrating the potential and practical value of deep learning technologies in intensive farming environments.

Supervised cryo-EM tomography denoising enabled by physics-based image simulation

Cryo-electron tomography enables three-dimensional visualization of biomolecules in their native aqueous state but suffers from low signal-to-noise ratio (SNR) due to electron dose limitations. Subtomogram averaging improves resolution but averages out structural heterogeneity, while self-supervised denoising risks over-smoothing fine details. Supervised learning has not been widely applied to cryo-ET because of the challenge of generating realistic training data. Here, we present a physics-informed supervised denoising framework based on a dedicated simulation pipeline. Microscope parameters, detector noise, and electron dose were extracted from experimental data, and clean images were generated through multislice calculations that solve the quantum-mechanical propagation of the electron wave function. Paired with noisy counterparts, these images were used to train a U-Net convolutional neural network. Applied to simulated and experimental tilt-series of liposomes, the method improved SNR by up to 132% and enhanced bilayer contrast without averaging, enabling recovery of fine structural details under low-dose conditions.

Evaluating model robustness in landslide susceptibility mapping using a unified data-consistent framework in northern Thailand

Abstract Landslide susceptibility mapping (LSM) is a critical tool for hazard mitigation in mountainous regions. However, the reliability of existing models remains uncertain due to inconsistencies in data quality, sampling strategies, and validation approaches. Although machine learning (ML) and deep learning (DL) models often report high predictive accuracy, their performance may not be robust or transferable, particularly in data-constrained tropical environments. To address this limitation, this study develops a unified, data-consistent evaluation framework to compare statistical, ML, and DL approaches for landslide susceptibility mapping in the Luang Prabang Range, northern Thailand. Five models, including Frequency Ratio (FR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), were applied using the same landslide inventory, conditioning factors, and validation strategy to ensure a fair comparison. The results indicate that RF achieved the most stable and reliable performance (AUC = 0.942), followed by XGBoost (0.930), CNN (0.914), LSTM (0.902), and FR (0.865). While DL models demonstrated strong predictive capability, their performance was more sensitive to data limitations and model configuration. In contrast, RF provided a better balance between accuracy, robustness, and data efficiency. The findings demonstrate that differences in model performance are influenced not only by algorithm selection but also by data structure and parameter settings. The main contribution of the study is the implementation of a unified evaluation framework that enables more reliable assessment of model robustness, uncertainty, and interpretability. This study provides practical guidance for model selection in landslide susceptibility mapping and highlights the importance of data consistency and model transparency, particularly in tropical mountainous regions where data limitations are common.

Investigating ChatGPT-mediated mind mapping to facilitate EFL learners’ reading comprehension

Mastering Reading Comprehension (RC) is a significant challenge for English as a Foreign Language (EFL) learners. This quasi-experimental mixed-methods study examined the potential effectiveness of a ChatGPT-mediated mind mapping technique in enhancing RC among EFL students at a public university in Saudi Arabia. Sixty male preparatory-year students were assigned to two groups: an experimental group (n = 30), which took part in a 10-week intervention in ChatGPT-mediated mind mapping, and a control group (n = 30), which was taught mind mapping through conventional methods. Data were gathered through pre- and post-tests of RC together with semi-structured interviews. Post-test RC scores were significantly higher in the experimental group than in the control group (U = 180.00, p < .001), with a medium-to-large effect size (r = 0.52). The qualitative data showed that students found the technique useful for breaking down complex ideas and for making the relationships between concepts in the text visible. At the same time, they reported difficulties with the accuracy of the AI output, with comprehending dense content, and with technology access. Read through the lenses of Sociocultural Theory and Cognitive Load Theory, the findings suggest that ChatGPT-mediated mind mapping can serve as a useful pedagogical tool for supporting RC. Teachers are therefore encouraged to incorporate the technique into their instructional practice while offering the support needed to address the challenges identified here.

Altermagnetic-RuO2-based all-magnetic tunnel junction: Giant and multistate tunneling magnetoresistances with perfect spin filtering

Altermagnets generate momentum-dependent spin-polarized currents without net magnetization, offering an intriguing platform for spintronic applications. However, altermagnet-based tunneling devices often possess limited magnetoresistance tunability or rely on conventional, highly spin-polarized ferromagnetic (FM) electrodes with stray fields. Here, we propose an all-magnetic tunnel junction paradigm by integrating experiment-feasible altermagnetic (AM) RuO2 electrodes with a FM/antiferromagnetic (AFM) CrOCl barrier. Utilizing density functional theory combined with the non-equilibrium Green's function methods, we demonstrate that the RuO2/CrOCl/RuO2 junction achieves multistate, nonvolatile spin transport through synergistic and antagonistic magnetization alignments between the AM source and the FM/AFM barrier. Remarkably, this design yields an exceptionally high tunneling magnetoresistance (TMR) of 1.8 × 105% and perfect spin filtering efficiency of ∼100%. The TMR is broadly tunable from 42% to 504% with a monolayer barrier and from 1% to ∼105% with a bilayer barrier. The resistance-area products from 0.74 × 10−3–4.5 × 10−3 Ω μm2 (monolayer) to 3.1 × 10−3–5.7 Ω μm2 (bilayer) lie within the practical range for magnetic memory applications. Our work establishes the fusion of AM electrodes with FM/AFM barriers as a feasible strategy to unlock multiple controllability and get rid of FM electrodes, opening avenues for high-performance multi-bit memory and reconfigurable logic devices.

Expired dapagliflozin as a promising corrosion inhibitor for copper in 1.0 M nitric acid: experimental and computational validation

Attack and defense networks in a student social system

Signed networks are a tool that researchers use to study the relationships between individuals in a complex system. Many studies focus on negative relationships and how these shape the structure of complex networks. Negative hubs, or nodes with the most negative connections, are of particular interest to researchers, as they help us understand social phenomena such as bullying, cyberbullying, and mental health. In this work, we study directed signed networks that represent positive (friendship) and negative (enmity) relationships between students of different academic levels. We propose a systematic methodology to obtain and analyze the networks of nine schools (∼4,300 students) in Yucatán, México. We introduce attack and defense subnetworks constructed from nodes with high out-negative and in-negative degree, respectively, and examine whether these subnetworks exhibit similar structural properties. Using the Leiden algorithm, we detected directed signed communities and compared the community structure of the attack and defense networks. We also calculated social balance theory measures and discussed the outcomes we obtained. We identify a range within which the definition of attack and defense networks is structurally meaningful. We have observed that the structural properties of attack and defense networks are similar and that they are not mutually exclusive, with an average of 60% common nodes, indicating a strong overlap between the two networks. Also, we have evaluated the attack and defense networks in relation to the gender of students, and found that female networks were generally denser across schools. Finally, we discuss the conceptual relationship between these networks and bullying-related roles.