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Thermochromic fiber-based self-powered detection system for maritime oil spill monitoring and fire safety applications
No full textMaritime oil spills and fire hazards pose significant threats to environmental safety, marine ecosystems, and critical infrastructure, demanding rapid and reliable detection strategies. In this work, we present a dualfunctional smart thermochromic fiber-based self-powered detection platform designed to address these urgent safety challenges. The system integrates poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) and polydiacetylene (PDA) composite nanofibers with triboelectric nanosensor (TENS) technology, enabling realtime detection for maritime oil spill monitoring and fire safety applications. The engineered nanofibers exhibit strong oleophilicity toward hydrocarbons and hydrophobicity toward water, allowing robust discrimination between oil and seawater through distinct triboelectric voltage signatures. Temperature-dependent studies reveal systematic modulation of work function and surface potential, resulting in enhanced triboelectric output during contact electrification. These findings are further supported by density functional theory (DFT) simulations, which confirm the temperature-induced changes in work function. Moreover, the TENS demonstrates a rapid response of 630 ms and sub-threshold multi-target sensing capabilities. When integrated with a wireless transmission module, it enables continuous autonomous monitoring without the need for external power sources. This makes the system particularly well suited for deployment in maritime environments and high-risk fire zones. Overall, this innovative sensing platform offers a promising approach for advanced environmental safety monitoring and has potential applications in smart cities, autonomous vehicles, and next-generation wearable safety devices, paving the way for real-time, distributed hazard detection and disaster prevention.
Catalytic enhancement on nanowire-engineered thermally stable oxide-metal inverse catalysts
No full textThe interface between metal and oxide plays a critical role in heterogeneous catalysis. In this study, we fabricated model inverse catalysts by transferring oxide nanowire assemblies of Co3O4, CeO2, and TiO2 onto Pt films to construct structurally well-defined oxide-metal interfaces. The deposited oxide nanowires retained their morphology without noticeable diffusion or sintering during annealing and carbon monoxide oxidation, as confirmed by near-edge X-ray absorption fine structure analysis. The surface electronic structure and morphology of the Pt film also remained largely unchanged, preserving the oxide-metal interfaces, which exhibited significant enhancements in turnover frequencies, depending on the oxide composition. These results highlight the utility of oxide nanowires in constructing thermally stable and composition-tunable oxide-metal interfaces for catalytic systems.
Genome-wide CRISPR screening identifies genes in recombinant human embryonic kidney 293 cells for increased ammonia resistance
No full textAmmonia, a byproduct of glutamine metabolism, inhibits cell growth and reduces product yield and quality in mammalian cell culture. To identify novel genes associated with ammonia resistance, a genome-wide CRISPR knockout screening was conducted in monoclonal antibody (mAb)-producing human embryonic kidney 293 (HEK-mAb) cells using a virus-free, recombinase-mediated cassette exchange-based gRNA interrogation method. The knockout cell library was subcultured for five consecutive passages under 20 mM NH4Cl, enriching cells with a sgRNA that conferred a proliferation advantage under high-ammonia conditions. Next-generation sequencing analysis of the enriched population identified three target genes -WNT3, TSPAN1, and CYHR1-among 19,114 genes. Knockout of these genes in HEK-mAb cells resulted in a 1.33- to 1.56-fold increase in maximum viable cell concentration and a 1.28- to 1.58-fold increase in maximum mAb concentration under 20 mM NH4Cl. Notably, WNT3 knockout maintained N-glycan galactosylation proportions of mAb despite ammonia stress. These findings highlight the effectiveness of genome-wide CRISPR knockout screening in identifying novel gene targets for ammonia-resistant HEK293 cell, offering a promising strategy for improving mAb production.
Multimodal understanding with GPT-4o to enhance generalizable pedestrian behavior prediction
No full textPedestrian behavior prediction is one of the most critical tasks in urban driving scenarios, playing a key role in ensuring road safety. Traditional learning-based methods have relied on vision models for pedestrian behavior prediction. However, fully understanding pedestrians' behaviors in advance is very challenging due to the complex driving environments and the multifaceted interactions between pedestrians and road elements. Additionally, these methods often show a limited understanding of driving environments not included in the training. The emergence of Multimodal Large Language Models (MLLMs) provides an innovative approach to addressing these challenges through advanced reasoning capabilities. This paper presents OmniPredict, the first study to apply GPT-4o(mni), a state-of-the-art MLLM, for pedestrian behavior prediction in urban driving scenarios. We assessed the model using the JAAD and WiDEVIEW datasets, which are widely used for pedestrian behavior analysis. Our method utilized multiple contextual modalities and achieved 67% accuracy in a zero-shot setting without any task-specific training, surpassing the performance of the latest MLLM baselines by 10%. Furthermore, when incorporating additional contextual information, the experimental results demonstrated a significant increase in prediction accuracy across four behavior types (crossing, occlusion, action, and look). We also validated the model s generalization ability by comparing its responses across various road environment scenarios. OmniPredict exhibits strong generalization capabilities, demonstrating robust decision-making in diverse and unseen driving rare scenarios. These findings highlight the potential of MLLMs to enhance pedestrian behavior prediction, paving the way for safer and more informed decision-making in road environments.
A simplified fragility analysis method for partially embedded structures considering out-of-plane localized bending of shear walls
No full textThis paper presents a numerical study on the seismic response and fragility analysis of a deeply embedded structure. Although considerable research has been conducted on seismic response analysis considering soil-structure interaction (SSI), limited studies have focused on deeply embedded structures. Recently, there has been growing interest in small modular reactors (SMRs), a new type of nuclear structure designed to be constructed with more than half of the structure embedded below the ground surface to avoid natural disasters and external threats. A seismic design considering SSI for these structures is required; however, since no SMR structures have been built yet, a conceptual box-shaped structure was used as a numerical model. The numerical model of the structure was modeled based on the conceptual design of the i-smr structure developed in Korea. In this study, a parametric study was carried out by varying the embedded depth to investigate its effect on the seismic response of the structure. Out-of-plane rotation is proposed as a damage index for deeply embedded structures to capture the characteristic shear wall displacement based on the seismic analysis results. In addition, the analysis cost of the seismic response of these structures is extremely high, which makes it difficult to apply existing seismic fragility analysis methods. To address this, a simplified fragility analysis methodology is proposed, which estimates failure probabilities for design-level earthquakes and derives fragility curves from three intensity measure values. This method provides a practical solution for evaluating the seismic fragility of deeply embedded structures.
Long-term enhancement of biogas production rate in anaerobic digestion of food waste using conductive carbon nanotubes media (CCM) at different organic loading rates
No full textIn this study, conductive carbon-nanotube media (CCM) were employed to promote direct interspecies electron transfer, aiming to enhance the methane production rate (MPR) and improve process stability during anaerobic digestion (AD) of food waste (FW). Batch experiments showed that supplementation with 0.2 g-CCM/g-VSS increased MPR by 23.3 %. Semi-continuous AD reactors revealed that the difference in MPR between reactors without CCM (R1) and those with CCM (R2) increased with rising organic loading rates (OLRs). At an OLR of 6.0 g-COD/L/d, R2 achieved a maximum MPR of 1.77 +/- 0.11 L-CH4/L/d, whereas the R1 failed due to rapid acidification and pH drop. Microbial analysis revealed enrichment of syntrophic bacteria and increased production of quinone-like extracellular polymeric substances in R2, which likely facilitated both direct and mediated electron transfer. These results indicate that CCM enhances MPR and operational stability, especially under high OLRs such as those found in FW.
Advances in biosensors for microbial biosynthesis of amino acids and their derivatives
No full textAmino acids and their derivatives play pivotal roles across diverse fields including biotechnology, pharmaceuticals, agriculture, and industrial manufacturing. The development of high-throughput screening methods for strains producing amino acids and their derivatives is crucial for both mining key enzymes and screening overproducers. This review systematically evaluates six classes of direct biosensors employed in the metabolic engineering of amino acid- or derivative-producing strains. These include biosensors based on transcription factors, riboswitches, Fo<spacing diaeresis>rster resonance energy transfer, circularly permuted fluorescent proteins, compoundinducible putative promoter regions, and protein translation elements. Their operational principles and recent advances in rational design, performance optimization, and practical implementation are critically analyzed. In addition, a systematic analysis of four categories of indirect biosensing strategies for the screening or regulation of amino acid- or derivative-producing strains is provided. These strategies target universal metabolic precursors, pathway-specific precursors, enzymatically transformed downstream metabolites, or competitive intermediates in branched pathways. Then, the design strategies, performance optimization methods, and practical implementation challenges of the existing biosensors are compared, which are accompanied by the discussion of the key parameters that are optimal for the biosensors applied in metabolic engineering. This work will facilitate the development of biosensors for metabolites that currently lack biosensing systems, and promote the innovation of the existing biosensors. These developments are expected to support efficient and sustainable production of amino acid-related compounds and other high-value metabolites.
Self-Wearing Adaptive Garments via Soft Robotic Unfurling
No full textRobotic dressing assistance has the potential to improve the quality of life for individuals with limited mobility. Existing solutions predominantly rely on rigid robotic manipulators, which have challenges in handling deformable garments and ensuring safe physical interaction with the human body. Prior robotic dressing methods require excessive operation times, complex control strategies, and constrained user postures, limiting their practicality and adaptability. This letter proposes a novel soft robotic dressing system, the Self-Wearing Adaptive Garment (SWAG), which uses an unfurling and growth mechanism to facilitate autonomous dressing. Unlike traditional approaches, the SWAG conforms to the human body through an unfurling-based deployment method, eliminating skin-garment friction and enabling a safer and more efficient dressing process. We present the working principles of the SWAG, introduce its design and fabrication, and demonstrate its performance in dressing assistance. The proposed system demonstrates effective garment application across various garment configurations, presenting a promising alternative to conventional robotic dressing assistance.
Predicting renewable energy stock volatility: A GARCH-CNN approach with indicator analysis
No full textThe renewable energy (RE) stock market is experiencing rapid growth, driven by environmentally conscious investors seeking to support a greener future while pursuing profitable opportunities. This study aims to forecast RE stock volatility, which is critical for managing risks in the RE stock market. We employ a Convolutional Neural Network (CNN) model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) forecasts to predict RE stock volatility. Three groups of indicators-internal stock, financial market, and policy uncertainty-are incorporated as additional inputs. The results demonstrate that integrating internal stock and financial market indicators significantly reduces prediction errors compared to the traditional GARCH model. Conversely, models incorporating the policy uncertainty indicator produce higher errors, suggesting that these indicators may introduce noise. SHapely Additive exPlanations (SHAP) analysis identifies the internal stock indicator, particularly the squared log returns of RE stocks, as a dominant factor, with the financial market indicator serving as a complementary factor. By integrating deep learning with econometric models, this study enhances the prediction of RE stock volatility and underscores the importance of selecting appropriate indicators. The findings provide valuable insights for investors and policymakers seeking to better understand and manage RE investment risks, highlighting the key drivers of RE stock volatility.
ACCys: A fluorescent probe for highly sensitive tandem detection of aluminum (III), copper (II), and cysteine
No full textAluminum and copper are associated with health risks and environmental concerns and have been mandated by WHO, among other agencies, as having a low limit of toxicity. Therefore, a simple and cost-effective method to detect and track Al3+ and Cu2+ in biological and environmental samples using fluorescence probe is paramount in biomedical research. In this study, we developed a novel Schiff base fluorescent probe ACCys (C17H16O4N2) bearing phenol and hydrazone groups for the selective and tandem detection of Al3+, Cu2+, and cysteine (Cys). ACCys exhibited a fluorescence "turn-on" response in the presence of [Al3+], a "turn-off" response in the presence of [Cu2+], and a fluorescence recovery with [Cys] addition. The binding stoichiometry, binding constants, and electronic properties were confirmed using spectroscopic and density functional theory (DFT) studies. ACCys demonstrated excellent sensitivity for Al3+ (Ka = 3.78 x 103 M-1), Cu2+ (Ka = 8.12 x 104 M-1), and Cys with the limit of detection of 72.7 nM, 140.6 nM, and 168 nM, respectively. Furthermore, ACCys successfully detected intracellular Al3+ ions in live-cell imaging and accurately quantified Al3+ levels in analytical water samples from different environmental sources. The selectivity, reversibility, and biocompatibility of ACCys make it a promising tool for real-time monitoring of Al3+ and Cu2+ in biological and environmental samples. The ability of ACCys to detect Cys with a fluorescence "turn-on" effect makes it unique and highly applicable for detecting multiple analytes with high selectivity and sensitivity.