Colloidal fouling remains an operational challenge for numerous membrane filtration processes. While various approaches exist for fouling mitigation, the use of time-periodic electric fields represents a particularly interesting, relatively unexplored one. Here, we report experimental results of colloidal deposition onto an electrically conductive membrane in the presence of AC electric fields of varying frequency. We show that fouling mitigation can be achieved in the presence of an AC electric field at a relatively low applied potential amplitude of 1 V. Moreover, an extremum in the frequency response is observed in the medium frequency range (represented by 100 Hz), where deposition is greatly reduced with slow kinetics and remains reversible, all while maintaining constant permeate flux. Our results serve as an initial, experimental proof of concept, demonstrating the potential of tuning AC fields for fouling control.

Conventional suturing and stapling cause additional trauma, pain, and cost for patients. As alternatives, existing bioadhesives suffer from imprecise fabrication, limited wet tissue adhesion, and insufficient biological functionalities for effective wound management. This work proposes biomimetic hydrogel bioadhesives composed of modified natural tannic acid (TA), hyperbranched polylysine (HPL), and acrylic acid (AA), abbreviated PTLAs, to offer solutions for tissue adhesion under challenging environments (underwater, body fluids, cold, pressure), and for enhanced healthcare. These PTLAs are fabricated via 3D printing, enabling the precise and controlled production of bioadhesives that are customized in a personalized manner with great reproducibility. Inspired by molluscs, developed PTLAs exhibit robust wet and underwater tissue adhesion, outperforming commercial and many recently reported bioadhesives. Ex vivo lamb and in vivo rat models demonstrate ultrafast (5 s) and efficient sealing and hemostasis. Exceptional freeze resistance and pressure resistance further expand their applicability to extreme environments. Meanwhile, coupled with superior infection resistance, PTLAs ensure enhanced wound healthcare while sealing and hemostasis. Further, their self-gelling feature supports dry powder adhesion/sealing applications, practical packaging, and long-term storage. Overall, adaptive tissue-like PTLAs present transformative potential as bio-tapes, bio-bandages, bio-sealants, bio-carriers, etc., paving the way for next-generation bioadhesives design and enhanced healthcare solutions.

The complicated interplay of clinical, demographic, and procedural factors makes it difficult to predict the success of in vitro fertilization (IVF), a commonly used assisted reproductive technology. The goal of this research was to create an artificial intelligence (AI) pipeline that could predict live birth outcomes in IVF treatments with high accuracy.

Design

We evaluated prediction performance by integrating different feature selection methods, such as principal component analysis (PCA) and particle swarm optimization (PSO), with different machine learning-based classifiers, including random forest (RF) and decision tree, as well as deep learning-based classifiers, including a custom transformer-based model and a Tab_transformer model with an attention mechanism. Additionally, this study analyzes confounding factors like patient age and previous IVF cycles and explores the influence of different perturbation and preprocessing techniques and validates the model’s robustness under varied scenarios. In addition, Shapley Additive Explanations (SHAP) analysis was performed to enhance interpretability of methods.

Results

This research demonstrated that the best performance was achieved by combining PSO for feature selection with the Tab_transformer-based deep learning model, yielding an accuracy of 97% and an AUC of 98.4%, highlighting its significant performance in prediction live births. By identifying the most significant predictors of infertility and guaranteeing clinical significance, SHAP analysis significantly improved interpretability.

Conclusion

With the accuracy and interpretability, this study develops a strong AI pipeline for predicting live birth outcomes in IVF. This study establishes a highly accurate AI pipeline for predicting live birth outcomes in IVF, demonstrating its potential to enhance personalized fertility treatments.

Compositional control is crucial in achieving the desired material properties across various applications, such as catalysis, energy storage, and semiconductor devices. Vapor phase infiltration (VPI) enables the direct transformation of polymeric nanostructures into inorganic nanostructures. However, conventional VPI processes typically utilize a single organometallic precursor, limiting the material synthesis to singular compositions. Here, we demonstrate a co-dosing VPI approach that overcomes this limitation by precisely controlling the partial pressures of the precursors, significantly expanding VPI capabilities for synthesizing composite and doped materials. Using trimethylaluminum (TMA) and diethylzinc (DEZ) precursors with an epoxidized isoprene (EPI) homopolymer and polystyrene-block-poly(epoxyisoprene) (PS-b-EPI) block copolymer films, we successfully fabricated Al-doped ZnO and AlO_x-ZnO composite materials with controllable aluminum (Al) incorporation ranging from 1.6 to 28%. Employing scanning transmission electron microscopy energy-dispersive X-ray spectroscopy cross-sectional analysis before and after thermal treatment, we identify characteristic infiltration and growth mechanisms of TMA versus DEZ, and we reveal differential growth behaviors between the homopolymer and block copolymer. The co-dosing approach demonstrated herein substantially broadens VPI applications, providing a versatile pathway for fabricating precisely tailored composites and doped nanostructures.

The present work focuses on highlighting bimolecular reactions in the NH₃ system involving HO₂, an important radical at intermediate combustion temperatures. The reaction mechanism generator (RMG) tool was used to identify potentially significant reactions, and the automated rate calculator (ARC) tool was used to automatically compute rate coefficients at the ΛCCSD(T)/aug-cc-pVTZ-F12//B2PLYP-D3/aug-cc-pVTZ level of theory. Several reactions explored in this work, such as N + HO₂ ⇌ NH + O₂, NH + HO₂ ⇌ NH₂ + O₂, NNH + HO₂ ⇌ N₂H₂ + O₂, and HNO₂ + HO₂ ⇌ NO₂ + H₂O₂, have not been thoroughly investigated in the existing literature. In particular, the reaction HNO + HO₂ ⇌ HNOH + O₂, though known, lacks a precise rate coefficient in recent chemical kinetic models for ammonia. This study provides computed rate coefficients for 10 hydrogen abstraction and disproportionation reactions involving HO₂ in the NH₃ system. The reaction rate coefficients computed here may improve future low- and intermediate-temperature oxidation models of NH₃.

Single-atom catalysts (SACs) possessing well-defined active sites of singular metal atoms have gained prominence in the field of electrocatalysis as they can be tuned to enhance activity and stability. In this study, a critical-raw-material (CRM)-free SAC is synthesized for the oxygen reduction reaction (ORR) in alkaline media by pyrolyzing polyimide nanoparticles and Fe without using a sacrificial template. Upon purification, the resulting catalyst demonstrates outstanding performance as cathodes in anion-exchange membrane fuel cells (AEMFCs) owing to sufficiently stabilized Fe single-atoms at the FeN₄ sites yielding a peak power density (P_max) as high as ∼1.8 W cm⁻² and specific power values up to 11.3 W ${\mathrm{mg}}_{\mathrm{PGM}}^{-1}$ ; the latter being the greatest reported among CRM-free cathode AEMFCs. The SAC also shows remarkable in situ durability under a very high current density of 1000 mA cm⁻², a first introduced here, with only a 2 mV h⁻¹ decay. Most impressively, when the SAC is combined with a NiMo anode to test a completely CRM-free high-temperature (HT)-AEMFC at 118 °C, a P_max of 372 mW cm⁻² and limiting current density of ∼1.14 A cm⁻² are achieved. This work represents a significant milestone in the development of durable SAC cathode catalysts for the next generation of CRM-free AEMFCs.

The robust titanium-based metal–organic framework MIL-125(Ti)-NH₂  was loaded with Cu²⁺ ions (2%, 5%) as well as by nanoparticles of Cu⁰ (5%). The ionic copper species were found to be atomically dispersed within the MOF pores, inducing a slight lattice expansion. In contrast, metallic copper was predominantly deposited at the MOF external surface, resulting in a more significant decrease in BET area. Optical spectroscopy and computational studies revealed that Cu⁰ modification led to a notable narrowing of the bandgap, attributed to the emergence of additional electronic states above the valence band edge, an effect not observed upon incorporation of Cu²⁺ ions. Photoluminescence spectroscopy showed reduced emission intensity upon copper incorporation, with the most pronounced quenching seen in Cu⁰-modified samples. This reduction is associated with both enhanced charge separation and non-radiative deactivation pathways, including phonon-assisted processes, as supported by time-dependent DFT calculations. Nanosecond transient infrared spectroscopy (TRIR) further demonstrated a ligand-to-metal charge transfer from the NH₂-BDC linker to the Ti–O cluster in pristine MIL-125(Ti)-NH₂  , which was extended through localized Ti–O–Cu interactions upon Cu incorporation. The TRIR measurements further indicated an electronic miniband located some 0.2-0.3 eV above Ec for all types of samples. Under visible light, the Cu⁰-loaded MOF outperformed the Cu²⁺-modified analogue in photocatalytic hydrogen evolution. The superior performance of metallic Cu⁰ over ionic Cu²⁺  may originated from reduced rate of radiative recombination as well as from its higher surface energy, which promotes the adsorption of water molecules.

Formic acid (HOCHO) is both a prototype oxygenated biofuel and a key intermediate in larger-scale combustion pathways, yet its pyrolysis and oxidation chemistry remains incompletely resolved. Previous recent chemical kinetic models of formic acid pyrolysis and oxidation have given two possibly contradicting impressions that either the kinetics are entirely known, or that automated tools for chemical kinetic model generation perform poorly for this system. Here, we demonstrate that an outof-the-box predictive chemical kinetic model, generated with minimal effort with no ad hoc fitting nor quantum-chemical refinement, reproduces jet-stirred-reactor speciation data as well as previous hand-tuned mechanisms across a wide range of temperatures (450–2000 K), pressures (1–50 bar), and equivalence ratios (ϕ = 0.4–2.0). We show that the apparent poor performance of an automatically-generated model reported previously [Energy Fuels 2022, 36, 14382–14392] disappears when the models are simulated under identical conditions using updated scripts, and we uncover that the high-pressure-limit rate coefficients adopted by Yin et al. [Combustion and Flame 2021, 223, 77–87] mask an unresolved, pressure-dependent branching between the decarboxylation (HOCHO <=> CO2 + H2) and dehydration (HOCHO <=> CO + H2O) channels. We also show that no model has successfully reproduced speciation data of formic acid under pyrolysis conditions, hence the current literature cannot offer a reasonable chemical kinetic model for this relatively small molecule. By highlighting these unaddressed challenges, we provide a clear roadmap for future refinements. This work shows that predictive, automated kinetic models are within reach for small oxygenated fuels, while pinpointing the CH2O2 potential energy surface as a key remaining challenge for accurately modeling formic acid pyrolysis and oxidation, and possibly other oxygenated fuels.

Stretchable epidermal electronics with stable electrical performance have been widely applied in numerous fields, including advanced medical therapy, wearable electronics, soft robotics, and human–machine interaction. However, conventional stretchable devices, which typically integrate a pliant substrate and a conductor, often encounter inferior electrical performance under sustained or intense stretching due to poor stretchability, limited permeability, and the notable disparity in Young's modulus between the substrate and the conductor. This mechanical discord intensifies problems such as reduced durability and inconsistent conductivity. In this work, we address these limitations by devising a liquid metal-based flexible conductor via an innovative direct coating method. This conductor, supported by an electrospun fiber nanomesh, reveals markedly enhanced permeability through a pre-stretch activation process. The resulting electrode demonstrates remarkable electrical conductivity reaching 3730 S cm⁻¹, superior permeability with a water vapor transmission rate of 40.2 g m⁻² h⁻¹, and extraordinary stretchability (>2000% strain), coupled with exceptional mechanical durability. The liquid metal fiber mat structure allows for the creation of breathable, on-skin electronics capable of long-term electrophysiological monitoring, rendering it ideal for continuous health monitoring applications.

Stretchable and moisture-permeable LM-SBS electrodes for high precise and long-term electrophysiological monitoring.

Precision and personalized medicine for disease management necessitates real-time, continuous monitoring of biomarkers and therapeutic drugs to adjust treatment regimens based on individual patient responses. This study introduces a wearable Microneedle-based Continuous Biomarker/Drug Monitoring (MCBM) system, designed for the simultaneous, in vivo pharmacokinetic and pharmacodynamic evaluation for diabetes. Utilizing a dual-sensor microneedle and a layer-by-layer nanoenzyme immobilization strategy, the MCBM system achieves high sensitivity and specificity in measuring glucose and metformin concentrations in skin interstitial fluid (ISF). Seamless integration with a smartphone application enables real-time data analysis and feedback, fostering a pharmacologically informed approach to diabetes management. The MCBM system’s validation and in vivo trials demonstrate its precise monitoring of glucose and metformin, offering a tool for personalized treatment adjustments. Its proven biocompatibility and safety suit long-term usage. This system advances personalized diabetes care, highlighting the move towards wearables that adjust drug dosages in real-time, enhancing precision and personalized medicine.