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This review examines recent progress in cobalt-based molecular catalysts for electrochemical water splitting as a sustainable route for green hydrogen (H2) production. H2 is highlighted as a clean energy carrier, with water splitting, comprising the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), offering an environmentally friendly production pathway. While noble metal catalysts exhibit high efficiency, their scarcity and cost drive the search for earth-abundant alternatives. Cobalt-based complexes have emerged as promising candidates due to their natural abundance, favorable redox properties, and variable valence states. The review discusses fundamental principles of water splitting and recent advances in catalyst design, emphasizing synthetic strategies such as structural engineering, morphological control, and compositional tuning to improve catalytic activity. Mechanistic insights into HER are analyzed in relation to catalyst structure and electronic properties. Remaining challenges, including stability, scalability, and efficiency, are also addressed, along with future research directions.

Examine the relationship between the inter-superior vena cava (SVC) distance and pulmonary blood flow (PBF) splitting, post-Fontan outcomes, hemodynamics, Fontan geometry, and pulmonary artery (PA) growth in bilateral bidirectional Glenn (BBDG) patients compared to unilateral bidirectional Glenn (BDG) patients. A single center retrospective cohort study comparing demographic, hemodynamic, and post-Fontan outcome variables between BBDG patients and a randomized cohort of BDG patients was conducted. A simple linear regression model was created to evaluate the relationship between Fontan geometry and PBF splitting. Cardiac magnetic resonance images were segmented using Slicer 5.6.2 and center line distance between the right and left SVC was calculated using an in-house code. The relationship between SVC distance and PBF was examined. The Nakata index was compared for BBDG and BDG patients. 42 patients (21 BBDG and 21 BDG) were included. Demographics, post-Fontan complications, and hemodynamics between groups were not different. PBF flow splitting increased as a function of inter-SVC distance. Patients with BBDG experienced a decrease in PA size over time with the mean difference in Nakata index between groups of 128.5 ± 23.73 (95% CI: 75.66, 181.4; p = 0.0003). Patients with BBDG have poor central PA growth compared to BDG patients. Although outcomes and hemodynamics were equivalent between groups, inter-SVC distance impacts PBF. This study provides a foundation on which to direct further prospective, studies of flow efficiencies in patients with BBDG circulations to guide patient-specific reconstruction techniques that maximize pulmonary artery growth potential and Fontan efficiency.

Parkinson's Disease (PD) is a progressive neurodegenerative disorder that causes motor and cognitive impairments, affecting approximately 1% of individuals over 60 years of age. Speech impairments are among the earliest and most accessible biomarkers, making voice-based assessment a promising avenue for remote PD monitoring. However, existing speech-based PD prediction methods suffer from feature redundancy that degrades model performance, non-Gaussian data distributions that violate model assumptions, and limited systematic feature grouping strategies. This study introduces an adaptive approach to improve PD diagnostic precision by predicting the Motor Unified PD Rating Scale (UPDRS) and Total-UPDRS scores from biomedical voice measurements. The proposed framework addresses these challenges through three integrated components: (1) Box-Cox transformation to stabilize variance, reduce skewness, and normalize features; (2) a clustering-based feature selection method that groups correlated features via K-Means and selects the most informative representative per cluster using mutual information, thereby eliminating redundancy without losing discriminative power; and (3) an Extra Trees Regressor (ETR) whose extreme randomization in node splitting provides computational efficiency and reduced variance. To ensure rigorous evaluation, a subject-independent data splitting strategy is adopted to prevent data leakage, and k-fold cross-validation is employed to assess model stability. The proposed method is compared against multiple feature selection techniques-mutual information, recursive feature elimination, Lasso regression, and autoencoders-paired with nine regression models including Ridge, Lasso, Linear, Decision Tree, k-Nearest Neighbors, Random Forest, Gradient Boosting, AdaBoost, and Extra Trees Regressors. The clustering-based feature selection combined with ETR yielded the best performance, achieving [Formula: see text] scores of 0.999 for Motor-UPDRS and 0.997 for Total-UPDRS on the test set. These results are further supported by cross-validation analysis and feature importance evaluation, demonstrating the effectiveness and robustness of the proposed framework for speech-based PD telemonitoring.

Crystals of an α-isocyanostilbene derivative show temperature-selective photomechanical behavior. At ambient temperature, they exhibit bending without splitting or moving under UV light, while below -50 °C, they show splitting upon photoirradiation. This successful control of the photomechanical response originates from the increased photodimerization yield upon cooling.

Development of an efficient and stable catalytic system for the production of sustainable hydrogen has remained a pivotal challenge in photocatalysis and electrocatalysis. In this work, we have reported a facile calcination strategy for synthesizing n-type BaCeO3@g-C3N4 heterojunctions that synergistically integrate the strong visible-light response of g-C3N4 with the high ionic conductivity and redox versatility of BaCeO3 perovskite. The BaCeO3@g-C3N4 heterojunction formation was confirmed through XRD, SEM, HR-TEM, and XPS techniques. The BaCeO3 nanoparticles were found to be evenly anchored on g-C3N4 nanosheets post-calcination as compared to non-calcined BaCeO3@g-C3N4. The as-prepared heterostructures exhibited remarkable activity under different water-splitting conditions. The optimized 20% BaCeO3@g-C3N4 demonstrated enhanced photochemical (PC) H2 production performance (15.21 mmol gcat-1 h-1) compared to pristine g-C3N4 and BaCeO3. Electrochemical (EC) and photoelectrochemical (PEC) investigations also corroborated its advanced HER activity at low overpotentials. The superior H2 evolution activity is attributed to the optimized band alignment and the interfacial charge transfer between g-C3N4 and BaCeO3. This work presents a multimodal H2 production activity of BaCeO3@g-C3N4 heterojunctions via photochemical and photo-/electrochemical methods.

Free-space optical communication (FSOC) terminals require rapid and accurate beam alignment. Conventional alignment schemes typically rely on beam splitting and dedicated sensing paths, which increase optical loss and system complexity. In this paper, we propose a common-path FSOC terminal enabled by a terraced multi-core fiber (MCF) and a gradient-descent laser nutation (GDLN) algorithm for fine alignment. The performance and operational field of view (FOV) of the proposed system were experimentally validated on a collimated-light tunnel platform, achieving a fine-alignment FOV of up to ±3.33 mrad. An outdoor experiment was further conducted between two buildings separated by 860 m. A six-axis motion platform applied external disturbances with an amplitude of 3° to the terminal at 4-hour intervals over a duration of 24 h. The system automatically realigned to restore the link within 4 s. Furthermore, real-time single-wavelength 10 Gbps IMDD transmission was demonstrated with a BER of 1.242×10-11.

Precise manipulation of on-chip optical modes using subwavelength structures is critical for miniaturizing devices and scaling high-speed optical interconnects, especially in Mode Division Multiplexing (MDM) systems. Leveraging advances in nanofabrication, devices engineered with subwavelength features enable versatile control over on-chip optical fields. Here, we propose a compact mode sorter based on a subwavelength waveguide grating, designed using the eigenmode expansion method. We analyze the optical field distribution at the focal plane of the on-chip lens for various incident modes. Implemented on a silicon-on-insulator (SOI) platform, the device achieves efficient separation of TE0, TE1, and TE3 modes, within a footprint of only 8.3 µm. When assembled into a complete (de)multiplexer, the device yields a 0.79 dB, 0.97 dB, and 0.91 dB insertion loss for the TE0, TE1, and TE3 channels, and 114 nm (1493-1607 nm) bandwidth for all channels with insertion loss is under 1.5 dB while crosstalk below 15 dB. Our simulation of fabrication tolerances demonstrates that this approach offers superior robustness compared to traditional devices. This work presents a generalizable design framework for metamaterial-based on-chip lenses using eigenmode analysis, paving the way for compact mode splitting and reconstruction applications.

Non-volatile optical switches are key components in programmable photonic integrated circuits. Here, we propose a 2×2 photonic switch based on an Sb2S3-embedded multimode interference waveguide crossing. The device routes light between reflection and transmission paths via Sb2S3 phase transitions facilitated by total internal reflection. Subwavelength tooth structures are incorporated on the reflecting facets to engineer the optical field distribution via the gradient-index effect. The device achieves an insertion loss of 0.61 dB and exhibits low crosstalk below -21 dB over a 300-nm bandwidth. Fabrication tolerances are evaluated through structural variations, indicating stable operation. In addition, multi-level tuning of the power splitting ratio is achieved, suggesting potential applications in reconfigurable photonic signal processing.

The Adolescent Brain Cognitive Development (ABCD) Study ® offers rich longitudinal data on environmental, genetic, and other factors related to substance use initiation. Classical marginal structural models (MSMs) require selecting covariates for propensity models, which is challenging in the presence of hundreds of correlated predictors. We analyzed longitudinal panel data from 11,868 ABCD participants, where each individual contributed repeated observations over time. Interval-level binary outcomes were defined for initiation of alcohol, nicotine, cannabis, and any substance, restricting analyses to participants at risk prior to initiation. All predictors were constructed as lagged variables to preserve temporal ordering. We implemented a two-stage machine learning-based causal framework. First, we performed graph discovery using a Granger-inspired lagged predictive modeling approach, applying elastic-net logistic regression to identify predictive relationships between lagged environmental variables and future initiation outcomes. Robust candidate edges were selected using subject-level bootstrap stability selection. Second, we estimated adjusted effect sizes for stable edges using double machine learning (DML)-style partialling-out with cross-fitting. For each candidate predictor, the treatment was defined as the lagged variable of interest and adjusted for high-dimensional lagged covariates. Cross-fitting with group-based splitting accounted for within-subject dependence, and nuisance functions were estimated using random forest models. Cluster-robust standard errors were used for inference. We identified a set of stable predictors across multiple domains, including sleep patterns, family environment, peer relationships, behavioral traits, and genetic risk. Many predictors were shared across substance outcomes, while some were outcome-specific. Estimated effect sizes were modest, typically ranging from -0.01 to 0.02 per standard deviation increase in the predictor. Both risk-increasing and protective associations were observed. Risk factors included sleep disturbance and behavioral risk indicators, while protective factors included parental monitoring and structured environments. This study provides a practical framework for analyzing high-dimensional longitudinal data and identifying time-varying predictors of substance use initiation. The approach combines machine learning for variable selection with causal inference methods for effect estimation. The results highlight both shared and substance-specific risk factors and identify modifiable targets, such as family environment and sleep, that may inform prevention strategies.

Clostridium butyricum Argonaute (CbAgo) is an emerging programmable nuclease with great potential in medical diagnosis and food analysis. However, the regulation of the CbAgo enzyme activity remains challenging. Here, we found that splitting the full-length guide DNA (gDNA) into two shorter strands could prevent the activation of CbAgo. Utilizing this characteristic, an Escherichia coli isotope, an E. coli O157:H7-triggered click reaction was designed to ligate split gDNA and activate CbAgo. As a result, a complementary molecular beacon (MB) was cut off to restore the fluorescence. And the fluorescence intensity is positively related to the concentration of the target pathogen. The whole reaction process takes only 105 min, and the limit of detection (LOD) is about 102 CFU/mL. This study provided a novel strategy for the detection of E. coli O157:H7 in a fast and simple manner, which also showed a great potential application in real samples.

Magnetic field enhancement represents an effective strategy to promote electrocatalytic water splitting, yet the mechanistic origin of asymmetric hydrogen evolution reaction (HER)/oxygen evolution reaction (OER) promotion remains poorly understood. Here, we investigate a bifunctional Fe0.5Rh0.5 (FeRh) single-crystal thin-film catalyst and demonstrate distinct asymmetric activity improvement under a 13 kOe magnetic field, ∼40% enhancement for HER and ∼32% for OER. Density functional theory and spin-resolved electronic structure analysis reveal that the field raises spin polarization by 1.2%, enhances density of states near the Fermi level, and accelerates interfacial charge transfer. The asymmetric enhancement stems from a more significant Gibbs free energy reduction for H adsorption than for OH**. Moreover, FeRh follows the oxide pathway mechanism to avoid excessive surface oxyhydroxide passivation, ensuring a stable magneto-responsive catalysis. This work clarifies the fundamental mechanism of asymmetric magnetic field promotion and provides a rational design for magnetically enhanced bifunctional electrocatalysts.

Urea oxidation reaction (UOR) is considered a promising alternative to the anodic oxygen evolution reaction (OER) with slow kinetics in water electrolysis due to its lower theoretical potential (0.37 V) compared with that of the OER (1.23 V). To design an efficient catalyst for UOR-coupled water electrolysis, in this work, a novel amorphous nickel hydroxide/lanthanum carbonate hydroxide composite catalyst (Ni(OH)2/La2(CO3)2(OH)2) with a unique cactus-like morphology was grown on a nickel foam (NF) via a one-step hydrothermal strategy. Ni(OH)2/La2(CO3)2(OH)2/NF exhibited a unique cactus-like morphology with 3D hierarchical heterostructures, which could expose more active sites and generate a large number of oxygen vacancies. The electron transfer from Ni to La and the synergistic effect of the amorphous/crystalline interface regulated the surface chemical environment of the catalyst, resulting in its enhanced electrocatalytic performance in the anodic UOR and cathodic HER. Therefore, the assembled two-electrode system for urea-assisted water electrolysis only required a cell voltage of 1.42 V to achieve a current density of 10 mA cm-2, which was significantly superior to that of overall water electrolysis. This work provides a new idea for exploring metal carbonate hydroxides with high activity as stable electrocatalysts for water splitting and other organic electro-oxidation reactions.