Browse Articles

Bio‐Inspired Site‐Specific Atomic Repair for Energy‐Efficient Regeneration of Spent LiFePO ₄ Batteries

ABSTRACT Solid‐state sintering regeneration offers a promising strategy for repairing spent lithium iron phosphate (LFP) cathodes, yet conventional homogeneous‐mixing (HM) sintering approaches neglect the intrinsic heterogeneity of FePO 4 within LFP particles. This induces additional long‐range Li +  solid‐state migration from Li‐rich to Li‐deficient domains during regeneration, creating substantial solid‐state diffusion barriers that necessitate extended high‐temperature sintering duration while triggering local over‐lithiation, ultimately degrading regeneration performance. Inspired by specific antigen‐antibody‐phagocyte interactions, we propose a novel mechanistic concept of site‐specific atomic repair (SAR) for energy‐efficient LFP regeneration. Through targeted‐adsorption‐enhanced evaporation‐nucleation processes, lithium sources and reductants are selectively anchored onto heterogeneous FePO 4 domains for localized repair, which shortens Li + solid‐state diffusion pathways, lowers migration barrier, and reduces FePO 4 → LiFePO 4 transition temperature from 300–400°C to 100–200°C. Consequently, the SAR‐regenerated LFP cathodes deliver enhanced performance while requiring only half of the high‐temperature sintering duration of conventional HM approaches, thereby achieving ∼20%–30% reduction in energy consumption & CO 2 emissions with a markedly improved profit by ∼40%. With additional heteroatom doping, SAR demonstrates exceptional rate performance (73.0 mAh g −1 at 15 C) and long‐term stability (90.0% after 600 cycles), ranking among the best reported to date, demonstrating cost‐effective mechanistic advances for industrial‐scale LFP recycling.

The use of cocoa pod shells as a bio-based concrete retarder

Low-level hydrogen co-firing in turkish thermal power plants: thermodynamic, environmental, and economic impacts

Computational exploration of urethane formation in the presence of amine catalysts

Abstract Polyurethanes are highly versatile polymers widely used in industry to produce a broad range of materials. Their synthesis from diisocyanates and polyols under industrial conditions typically requires a catalyst—or a combination of catalysts—which plays a critical role in determining reaction efficiency and selectivity. In this study, we examined a diverse set of amine catalysts to identify key properties relevant to selecting the most suitable species for specific applications. To this end, we evaluated both structural and energetic descriptors, namely steric parameters (expressed as %Vbur) and proton affinities (PA). The results show that 1-(3-aminopropyl)imidazole (APIM) possesses the lowest calculated proton affinity (905.8 kJ mol − 1 ) among the studied compounds, indicating a greater tendency of its conjugate acid to donate a proton. In contrast, steric analysis revealed that tertiary amines generally exhibit higher buried volumes than primary and secondary amines. Notably, the differences in %VBur values between 2,2’-dimorpholinodiethylether (DMDEE) (67.0%), dimethylaminoethylether (DMAEM-1 N*) (66.4%), and N -Ethylmorpholine (NEM*) (65.8%) are relatively small; these values indicate similar steric environments, rather than a strictly greater steric effect for one catalyst over the others. However, given the small numerical differences, these catalysts can be considered to exhibit comparable steric profiles. Overall, this work provides deeper insight into how electronic and steric factors influence urethane formation and offers a rational basis for selecting appropriate catalysts tailored to specific applications.

Relationship between motor reserve, general cognitive functioning and brain atrophy in older adults

Diabetes–TyG interaction and mortality in critical cerebrovascular disease: a retrospective cohort study

Changes in public perceptions of wild boar presence and management in Flanders

A high-performance dataflow-driven 2-D transform architecture for versatile video coding

Validation of portable, semi-dry electrode-based electroencephalography device for its application in brain–computer interface solutions

Abstract In recent years, commercial lightweight electroencephalography (EEG) headsets are gaining popularity in neuroscience. These devices commonly utilize only a few dry electrodes in specific locations and signal quality is often inferior compared to that of their traditional counterparts. In this study, we wanted to assess the feasibility of portable, paste-less, passive electrode-based EEG headset MindRove vision (VSN) for laboratory use. Three paradigms were implemented for acquiring visual evoked potential (VEP), P300 event-related potential and motor execution task (ME) related cortical patterns. Measurements were taken by using VSN, with wet-electrode system mBrainTrain SMARTING applied as reference. The performance of the devices was assessed by using signal-to-noise ratio (SNR) for VEP and P300 while support vector machine, random forest and convolutional neural network-based classifiers were fit to ME data. The SNRdB (i.e. SNR expressed in decibels) of VSN was greater for both VEP and P300, by a margin of 1.998 and 2.845 dB, respectively. There was a significant difference between VEP signal amplitude levels and SNRdB, P300 SNR and SNRdB in favor of VSN. Average accuracy of the sorters were 78.8% for VSN and 80.9% for SMARTING; the difference was not significant. The application of VSN is feasible for use in research besides qualitative exploration.

Correlation between NRS-2002 scores and PD-1/CTLA-4 expression levels in patients with community-acquired pneumonia

Dermoscopy guided high-frequency ultrasound features of basal cell carcinoma subtypes compared with histopathology: a multicenter study

Abstract Accurate non-invasive differentiation of basal cell carcinoma (BCC) subtypes is crucial for guiding treatment; however, current imaging tools have limitations in accessibility, depth resolution, and interpretability. To describe sonographic features of distinct BCC subtypes using dermoscopy-guided high-frequency ultrasound (DG-HFUS), and assess its potential as a non-invasive subtyping modality. In this multicenter cross-sectional study, 87 histologically confirmed BCCs (nodular, superficial, infiltrative, and micronodular) were imaged using a portable 33 MHz DG-HFUS device. Morphological features were evaluated independently by blinded observers, and statistical associations with histologic subtypes were analyzed using univariate logistic regression. Hypoechoic mass was the most common sonographic feature of BCCs, seen in 92% of cases. Distinct DG-HFUS features correlated with specific subtypes: ribbon-like shape (OR = 86.3, p  < 0.0001), homogeneous internal echoes (OR = 22.5, p  = 0.003), and well-defined margins (OR = 9, p  = 0.04) were strongly associated with superficial BCC; oval shape (OR = 3.8, p  = 0.003) and prominent hyperechoic spots (OR = 3.5, p  = 0.005) with nodular BCC; and acoustic shadowing (OR = 7.5, p  = 0.001) with infiltrative BCC. Micronodular BCCs were underrepresented. Mixed subtypes were excluded, and vascular patterns were not evaluated. DG-HFUS reveals subtype-specific sonographic features and offers a promising, accessible, and interpretable tool for non-invasive BCC classification in clinical settings.

Comprehensive UPLC-QTOF-MS based metabolic profiling and insights into the bioactive potential of Vanda tessellata: a medicinal orchid

EEG-based automated evaluation of automotive sound quality using ensemble deep learning

Abstract The evaluation of automotive sound quality is of considerable significance for improving driving comfort. However, existing methodologies suffer from notable limitations, including inconsistencies in subjective evaluations and weak correlations between objective metrics and auditory perception. In response to these challenges, an automated evaluation method incorporating electroencephalogram (EEG) signals and ensemble deep learning is proposed herein. Initially, EEG data is acquired from 30 subjects during exposure to 16 automobile sounds with sporty quality. Subsequently, the LSTMS-B model is incorporating Swish activation into LSTM to mitigate gradient vanishing and enhancing Bagging through optimized majority voting, achieving 90.8% accuracy with superior performance over conventional LSTM variants; Furthermore, an innovative ResNet-based regression model is developed to establish the automobile sound-EEG feature mapping, enabling the LSTMS-B model to achieve 89.75% average F1 score in sound quality classification using brain auditory representations while reducing reliance on conventional EEG paradigms. This study develops a novel sound quality evaluation paradigm through deep-ensemble learning integration, where the proposed cross-modal feature mapping method provides a transferable AI framework for interpreting human auditory perception mechanisms.

Automated quantification of stereotypical motor movements in autism using persistent homology

BCG vaccination at birth shapes the TCR usage and functional gene expression profile of MR1T cells at 9 weeks of age

Exploring the role of a social robot in building a sense of connection

Abstract An important but under-explored use case for socially assistive robotics is in community formation. In this work, we conduct a novel investigation into the ability of a social robot to encourage human-human relationships among users. We conduct a between-subjects Wizard-of-Oz experiment in which we focus on the robot’s role in interaction as a determinant in the sense of connection that participants feel as a result of interacting with the robot. N=86 college-age participants were recruited to watch a video with a robot, where the robot’s group membership and behavior were adjusted to reflect different roles. We vary the robot’s group membership along three levels: the same group as the participants, engaging in the same activity as the participants, or simply being in the same room and acknowledging the participants, with a control condition of no robot present. Before, during, and after the video, the robot would speak to encourage interaction between participants and the robot when applicable. After the video was concluded, participant sense of connection was measured through a series of Likert surveys. We find that inter-user conversation increased when the robot actively participated in the video viewing and asked questions of the users compared to when the robot did not. We also find some exploratory evidence that these effects might extend to user attitudes about not only each other but also the laboratory running the experiment.

Veno-arterial ECMO as a bridge to delayed repair in post-infarction ventricular septal rupture with cardiogenic shock

Modeling of the electrostatic field generated by horizontal current sources in a sea-ice-covered four-layer medium

Hydrogenation of nitriles to primary amines by a phosphine-free cobalt(II) complex in the absence of H2 gas

Exploratory analysis of genomic prediction profiles in Arequipa fighting cattle using commercial SNP panels