Search Results (113)

In most commercial lithium-ion batteries, graphite remains the primary anode material. However, its theoretical specific capacity is only 372 mAh∙g⁻¹, which falls short of meeting the demands of high-performance electronic devices. Silicon anodes, despite boasting an ultra-high theoretical specific capacity of 4200 mAh∙g⁻¹, suffer from significant volume expansion (>300%) during cycling, leading to severe capacity fade and limiting their commercial viability. Currently, silicon-based polymer-derived ceramics have emerged as a highly promising next-generation anode material for lithium-ion batteries, thanks to their unique nano-cluster structure, tunable composition, and low volume expansion characteristics. The maximum capacity of the ceramics can exceed 1000 mAh∙g⁻¹, and their unique synthesis routes enable customization to align with diverse electrochemical application requirements. In this paper, we present the progress of silicon oxycarbide (SiOC), silicon carbonitride (SiCN), silicon boron carbonitride (SiBCN) and silicon oxycarbonitride (SiOCN) in the field of LIBs, including their synthesis, structural characteristics and electrochemical properties, etc. The mechanisms of lithium-ion storage in the Si-based anode materials are summarized as well, including the key role of free carbon in these materials. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Deep learning approaches for pork freshness grading typically require large datasets, which limits their practical application due to the high costs associated with data collection. To address this challenge, we propose BBSNet, a lightweight few-shot learning model designed for accurate freshness classification with a limited number of images. BBSNet incorporates a batch channel normalization (BCN) layer to enhance feature distinguishability and employs BiFormer for optimized fine-grained feature extraction. Trained on a dataset of 600 pork images graded by microbial cell concentration, BBSNet achieved an average accuracy of 96.36% in a challenging 5-way 80-shot task. This approach significantly reduces data dependency while maintaining high accuracy, presenting a viable solution for cost-effective real-time pork quality monitoring. This work introduces a novel framework that connects laboratory freshness indicators to industrial applications in data-scarce conditions. Future research will investigate its extension to various food types and optimization for deployment on portable devices. Full article

The growing demand for healthy and sensorially pleasing foods is accompanied by increasing sustainability concerns among consumers and industry. Therefore, exploring native and underutilized resources for traditional preparations is important. This study evaluated the incorporation of Moringa oleifera leaves and baru almonds (Dipteryx alata) in pesto sauce, comparing them to the traditional recipe regarding composition, color, total phenolics, volatiles, sensory characteristics, and acceptability. The following four formulations were developed: basil with cashew nuts (B/CN); basil with baru almonds (B/BA); and two versions with 50% basil replaced by moringa, combined with cashew (BM/CN) or baru (BM/BA). BM/BA presented the highest protein content (9.0%), compared to B/CN (7.9%). BM/CN showed a greener color. BM/CN and BM/BA showed total phenolics and antioxidant capacities similar to B/CN. BM/BA showed elevated condensed tannins (113.28 mg CE/100 g). All samples contained 1,8-Cineole and linalool, key to the aroma of basil. Pesto with moringa and/or baru showed good sensory acceptance, rated as “liked moderately”, with no difference from the conventional version (p > 0.05). There were no differences in the basil aroma, nutty flavor, or greasiness. Pesto sauce is a promising matrix for incorporating regional, underused ingredients such as moringa leaves and baru almonds, expanding their potential in new food development. Full article

Precise quality screening of tea tree seeds is crucial for the development of the tea industry. This study proposes a high-precision quality classification method for tea tree seeds by integrating mid-infrared (MIR) spectroscopy with an improved deep learning model. Four types of tea tree seeds in different states were prepared, and their spectral data were collected and preprocessed using Savitzky–Golay (SG) filtering and wavelet transform. Aiming at the deficiencies of DenseNet121 in one-dimensional spectral processing, such as insufficient generalization ability and weak feature extraction, the ECA-DenseNet model was proposed. Based on DenseNet121, the Batch Channel Normalization (BCN) module was introduced to reduce the dimensionality via 1 × 1 convolution while preserving the feature extraction capabilities, the Attention–Convolution Mix (ACMix) module was integrated to combine convolution and self-attention, and the Efficient Channel Attention (ECA) mechanism was utilized to enhance the feature discriminability. Experiments show that ECA-DenseNet achieves 99% accuracy, recall, and F1-score for classifying the four seed quality types, outperforming the original DenseNet121, machine learning models, and deep learning models. This study provides an efficient solution for tea tree seeds detection and screening, and its modular design can serve as a reference for the spectral classification of other crops. Full article

The development of photothermal-assisted photocatalytic systems with broad-spectrum solar utilization and high charge separation efficiency remains a critical challenge for antibiotic degradation. Herein, we report novel black g-C₃N₄ (BCN) materials synthesized via a one-step thermal copolymerization strategy using C/N precursors and tetrachlorofluorescein. After the introduction of tetrachlorofluorescein, the color of the sample changes, which gives BCN enhanced light absorption and a significant photothermal effect for poorly heating-assisted photocatalysis. The synergistic coupling of photothermal and photocatalytic processes enabled the optimal BCN-U sample to achieve exceptional degradation efficiency (89% within 120 min) for a typical antibiotic (e.g., tetracycline) under an LED lamp as the visible light source, outperforming conventional yellow g-C₃N₄ (YCN-U) by a factor of 1.37. Mechanistic studies revealed that the photothermal effect facilitates carrier separation via thermal-driven electron excitation while accelerating reactive oxygen species (•OH and •O₂⁻) generation. The synergistic interplay between photocatalysis and photothermal effects, which improved mass transfer, ensures robust stability, which provides new insights into designing dual-functional carbon nitride-based materials for sustainable environmental remediation. Full article

Carbon fiber-reinforced SiBCN ceramic matrix composite ($C_f$/SiBCN CMC) is emerging as a promising candidate for advanced thermal protection systems, owing to its superior thermal stability and notable ablation resistance. In this study, the mechanical properties of $C_f$/SiBCN CMC are investigated theoretically and experimentally. Utilizing a multiscale approach, a representative volume element (RVE) is developed to predict mechanical properties based on detailed microstructural characterization. The predictions derived from the RVE demonstrate agreement with the experimental findings. The experimental results show dispersion in the mechanical properties of $C_f$/SiBCN CMC. To investigate whether the dispersion of mechanical properties is associated with defects, this study examines the impact of the location, content, and size of defects on the mechanical properties of the $C_f$/SiBCN CMC. The analysis reveals that the location, content, and size of defect all impact the mechanical properties of the $C_f$/SiBCN CMC, with overall porosity having the most significant effect. When the porosity is constant, variations in defect location and size also contribute to the observed variability in the mechanical performance of the $C_f$/SiBCN CMC. Full article

Borocarbonitrides (BCNs), a new class of ternary materials combining boron, carbon, and nitrogen atoms, have emerged as promising candidates in decarbonization technologies due to their unique physicochemical properties. BCNs offer an adjustable atom composition and electronic structure, thermal stability, and potentially a large specific surface area, which are attractive features for efficient interactions with carbon dioxide. These make BCNs suitable for carbon dioxide capture, storage, and catalytic conversion applications. Furthermore, BCNs have the potential to (electro)catalyze the synthesis of green fuels, such as hydrogen, as well as that of other hydrogen carriers such as ammonia. With this review, we examine the recent advances in BCN synthesis methods, characterization, and functional applications while focusing on their role in the decarbonization technologies mentioned above. We aim to highlight the potential of BCNs to drive innovation in sustainable carbon management. Additionally, in the last section of this paper, we discuss the challenges and prospects of BCNs in decarbonization and beyond. Full article

Objective: This study compares machine learning (ML) and logistic regression (LR) algorithms in developing a predictive model for sPCa using the seven predictive variables from the Barcelona (BCN-MRI) predictive model. Method: A cohort of 5005 men suspected of having PCa who underwent MRI and targeted and/or systematic biopsies was used for training, testing, and validation. A feedforward neural network (FNN)-based SimpleNet model (GMV) and a logistic regression-based model (BCN) were developed. The models were evaluated for discrimination ability, precision–recall, net benefit, and clinical utility. Both models demonstrated strong predictive performance. Results: The GMV model achieved an area under the curve of 0.88 in training and 0.85 in test cohorts (95% CI: 0.83–0.90), while the BCN model reached 0.85 and 0.84 (95% CI: 0.82–0.87), respectively (p > 0.05). The GMV model exhibited higher recall, making it more suitable for clinical scenarios prioritizing sensitivity, whereas the BCN model demonstrated higher precision and specificity, optimizing the reduction of unnecessary biopsies. Both models provided similar clinical benefit over biopsying all men, reducing unnecessary procedures by 27.5–29% and 27–27.5% of prostate biopsies at 95% sensitivity, respectively (p > 0.05). Conclusions: Our findings suggest that both ML and LR models offer high accuracy in sPCa detection, with ML exhibiting superior recall and LR optimizing specificity. These results highlight the need for model selection based on clinical priorities. Full article

ETV6::RUNX1-positive pediatric acute lymphoblastic leukemia frequently has a prenatal origin and follows a two-hit model: a first somatic alteration leads to the formation of the oncogenic fusion gene ETV6::RUNX1 and the generation of a preleukemic clone in utero. Secondary hits after birth are necessary to convert the preleukemic clone into clinically overt leukemia. However, prenatal factors triggering the first hit have not yet been determined. Here, we explore the influence of maternal factors during pregnancy on the prevalence of the ETV6::RUNX1 fusion. To this end, we employed a nested interventional cohort study (IMPACT-BCN trial), including 1221 pregnancies (randomized into usual care, a Mediterranean diet, or mindfulness-based stress reduction) and determined the prevalence of the fusion gene in the DNA of cord blood samples at delivery (n = 741) using the state-of-the-art GIPFEL (genomic inverse PCR for exploration of ligated breakpoints) technique. A total of 6.5% (n = 48 of 741) of healthy newborns tested positive for ETV6::RUNX1. Our multiple regression analyses showed a trend toward lower ETV6::RUNX1 prevalence in offspring of the high-adherence intervention groups. Strikingly, corticosteroid use for lung maturation during pregnancy was significantly associated with ETV6::RUNX1 (adjusted OR 3.9, 95% CI 1.6–9.8) in 39 neonates, particularly if applied before 26 weeks of gestation (OR 7.7, 95% CI 1.08–50) or if betamethasone (OR 4.0, 95% CI 1.4–11.3) was used. Prenatal exposure to corticosteroids within a critical time window may therefore increase the risk of developing ETV6::RUNX1+ preleukemic clones and potentially leukemia after birth. Taken together, this study indicates that ETV6::RUNX1 preleukemia prevalence may be modulated and potentially prevented. Full article

To address challenges in antibiotic wastewater treatment, we synthesized a series of graphitic carbon nitride (g-C₃N₄)-based photocatalysts (BCN, PCN, TCN, BTCN, and TCNE-modified PTCN) via defect engineering. TCNE modification disrupted the triazine ring-bridging amino network in PTCN, forming a porous structure with enhanced specific surface area validated by SEM/TEM while retaining the graphene-like framework confirmed by XRD/FTIR. Photoluminescence (PL) analysis revealed prolonged photogenerated carrier lifetime and improved separation efficiency in PTCN, achieving 89.10% degradation of chlortetracycline hydrochloride under visible light—1.65-fold higher than pristine g-C₃N₄. Mechanistic studies identified superoxide radicals (•O₂⁻) as dominant active species, generated via O₂ activation at defect sites and efficient electron-hole utilization. Optimized conditions enabled PTCN to maintain high activity across a broad pH range and retain 82.59% efficiency after five cycles. This work advances defect-engineered photocatalyst design for adaptable, high-performance antibiotic degradation, offering practical insights for wastewater remediation. Full article

Hexagonal BCN (h-BCN), an isoelectronic counterpart to graphene, exhibits chirality and offers the distinct advantage of optical activity in the vacuum ultraviolet (VUV) region, characterized by significantly higher wavelengths compared to graphene nanoflakes. h-BCN possesses a wide bandgap and demonstrates desirable semiconducting properties. In this study, we employ Density Functional Theory (DFT) calculations to investigate the proximity effects of adsorbed h-BCN flakes on two-dimensional (2D) substrates. The chosen substrates encompass monolayers of 3D transition metals and WSe₂, as well as a bilayer consisting of WSe₂/Ni. Notably, the hydrogen-terminated h-BCN nanoflakes retain their planar configuration following adsorption. We observe a strong interaction between h-BCN and fcc-based monolayers such as Ni(111), resulting in the closure of the optical bandgap, while the adsorption energy on WSe₂ is significantly weaker, preserving an approximate 1.1 eV bandgap. Furthermore, we demonstrate the magnetism induced by the proximity of adsorbed chiral h-BCN molecules, and the chiral-induced spin selectivity within the proposed systems. Full article

Background/Objectives: Validation of predictive models (PMs) is crucial to be implemented in new populations or when advances in diagnostic approaches occurred. The aim of this study is to validate the BCN-MRI PM for sPCa when a highly effective prostate biopsy protocol is used. Methods: A prospective cohort of 457 men suspected of having PCa, for whom MRI results were reported with the Prostate Imaging-Reporting and Data System (PI-RADS) v 2.1, underwent a per 0.5 mm-core mapping targeted biopsy of suspicious lesions and perilesional areas, followed by a 12-core-systematic biopsy. These procedures took place between 1 February 2022, and 29 February 2024, at a reference center for prostate biopsy. The individual likelihood of sPCa was assessed through the BCN-MRI risk calculator. Results: The overall sPCa detection rate was 58.3%. The calibration curve of the BCN-MRI PM showed an appropriate accuracy between expected and observed probabilities with a discrimination ability for sPCa yielding an area under the curve (AUC) of 0.862 (95% CI 0.828–0.896) comparable to the AUC of 0.858 (95% CI 0.833–0.883) observed in the development cohort. The application of the BCN-MRI PM provided a net benefit over performing biopsies on all men, avoiding 24.9% of prostate biopsies at 95% sensitivity for sPCa, compared to the 23.7% reduction observed in the development cohort. Conclusions: We conclude that the BCN-MRI PM is ready to be implemented when this biopsy protocol is employed. Full article

As a graphene-like material, h-BN has stimulated great research interest recently due to its potential application for next-generation electronic devices. Herein, a systematic theoretical investigation of electronic structures and optical properties of C-doped and Cu-Al co-doped h-BN is carried out by the first-principles calculations. Firstly, two different C-doped h-BN structures for the para-position and ortho-position are constructed. The results show that the C ortho-doped h-BN (BCN) structure with a band gap of 3.05 eV is relatively stable, which is selected as a substate to achieve the Cu-Al co-doped h-BN. Based on this, the effect of the concentration of C atom doping on the electronic and optical properties of Cu-Al co-doped BC_xN (x = 0, 11.1% and 22.2%) is investigated. The results demonstrate that the band gap of Cu-Al co-doped BC_xN decreases and the optical properties improve with the increase in C atom concentration. The band gap and static dielectric constant of Cu-Al co-doped BC₀N, BC₁N and BC₂N are 0.98 eV, 0.87 eV and 0.23 eV and 2.34, 3.03 and 3.77, respectively. As for all Cu-Al co-doped BC_xN systems, the adsorption peak is red-shifted, and the peak intensity obviously decreases compared to the undoped h-BN. Additionally, the Cu-Al co-doped BC₂N exhibits the best response to visible light. This work will provide valuable guidance for designing and developing h-BN-based doping systems with good performance in the field of optical and photocatalysis. Full article

The photocatalytic conversion of ethanol and the simultaneous development of hydrogen technology play a role in solving the energy crisis and reducing environmental pollution. In this research, rod-like M-MoS₂ serves as a channel for charge transfer, leading to superior photocatalytic activity compared to H-MoS₂. Further, two-dimensional (2D) B-doped C₃N₄ (BCN) nanosheets were anchored on the one-dimensional (1D) rod-like M-MoS₂ surface to form a 1D/2D heterojunction, with M-MoS₂/BCN-0.08 (mass ratio of M-MoS₂:BCN of 0.08:1) exhibiting the highest photocatalytic performance. Under visible light irradiation, the ethanol conversion rate reached 1.79% after 5 h of photocatalytic reaction per gram of catalyst, while generating 421 μmol of 2,3-butanediol (2,3-BDO), 5460 μmol of acetaldehyde (AA), and 5410 μmol of hydrogen gas (H₂). This different characterization provides evidence that a significant amount of photoinduced electrons generated in BCN under illumination conditions rapidly transfer to the conduction band (CB) of M-MoS₂ through the rod-like structure of M-MoS₂, and finally transfer to Pt to promote the production of hydrogen gas. The photoinduced holes in the valence band (VB) of M-MoS₂ are rapidly consumed by ethanol upon transferring to BCN, effectively separating the photoinduced electron–hole pairs and resulting in superior photocatalytic performance. Full article