Microwave ultra-high temperature (UHT) processing is promising for liquid food sterilization, yet its inactivation mechanisms against thermophilic bacteria remain inadequately understood. This study investigated the bactericidal efficacy and mechanisms of microwave UHT treatment against vegetative cells of Geobacillus stearothermophilus ATCC 7953. Using a single-mode microwave system at 2450 MHz, bacterial suspensions were subjected to various power-time combinations achieving 136 ± 1 °C followed by immediate cooling. A nonlinear relationship between power and inactivation efficacy was observed: optimal reductions of approximately 5 log CFU∙mL^–1 were achieved at 150 W∙mL^–1 for 40 s and 300 W∙mL^–1 for 20 s, while intermediate powers yielded inferior outcomes. Mechanistic analyses revealed that microwave treatment induced significant membrane damage, suppressed metabolic activity, and dramatically elevated intracellular reactive oxygen species and malondialdehyde levels, with the 300 W∙mL^–1 treatment generating the highest oxidative stress. Scanning electron microscopy confirmed distinct morphological alterations without electroporation. The similar trends observed between oxidative markers and bactericidal efficacy suggest that oxidative stress-mediated lipid peroxidation may constitute a primary inactivation mechanism, with low-power prolonged exposure promoting cumulative damage and high-power short-duration treatment triggering acute oxidative burst. These findings elucidate the power-time synergistic mechanism of microwave UHT inactivation and provide a theoretical foundation for process optimization.

Droplets exhibit distinct wetting characteristics and transport behavior on solid substrates at the macroscopic and microscopic scales. This behavior is critical for understanding liquid mass transfer on fibrous membranes. To better understand and control the mass transfer of liquids on fibrous membranes, we combined in situ visualization techniques with multiphase flow simulations. This approach enabled us to systematically explore the effects of various factors, including fiber wettability, fiber diameter, and fiber spacing, on liquid transfer behavior. Furthermore, we successfully elucidated the transfer mechanisms governing droplet transport on individual fibers and between adjacent fibers. Based on our findings, we constructed composite fiber membranes with varying fiber diameters and wettability structures. The validity of the proposed approach was verified by comparing fog collection, droplet wetting, and liquid permeation efficiency. Consequently, this study establishes a transferable cross-scale framework and proposes a general design strategy for constructing fibrous membranes tailored to diverse application requirements.

Converting CO₂ into CO via reverse water gas shift (RWGS) reaction is a key step for carbon recycling. Molybdenum trioxide (MoO₃) is a promising precatalyst due to its high activity and near-unity CO selectivity, yet the role of support properties remains unclear. To address this, a series of MoO₃-based catalysts supported on MgO, γ-Al₂O₃, SiO₂, TiO₂, ZrO₂, and CeO₂ were prepared. Systematic characterizations show that MoO₃ undergoes in situ carburization to Mo₂C, and the extent of carburization correlates positively with catalytic activity. The formation of active Mo₂C is governed by the metal oxide-support interaction (MOSI): strong MOSI between MoO₃ and basic supports (MgO, CeO₂) promotes stable solid solutions that suppress carburization, whereas acidic and amphoteric supports preserve MoO₃ crystallites, enabling efficient carburization and high RWGS performance. Among all catalysts, MoO_xC_y/SiO₂ exhibits the weakest MOSI, the highest surface Mo₂C concentration, and thus superior mass-specific activity. The ionic potential of the support serves as a descriptor for MOSI strength, while the specific surface area introduces a certain deviation for amphoteric supports (γ-Al₂O₃, TiO₂, ZrO₂). This work provides clear support selection criteria and a theoretical foundation for rational design of high-performance Mo-based RWGS catalysts.

Driven by the urgent demands for process efficiency, operational safety, and industrial intelligence, piping and instrumentation diagrams are evolving from static design documents into dynamic knowledge-intensive carriers. However, the intelligent digitization of piping and instrumentation diagrams faces systemic challenges due to their highly unstructured data format, dense symbolic information, and heterogeneous drafting styles, which hinder automated information extraction and result in a fragmented data ecosystem. Recent advancements in artificial intelligence, particularly in pattern recognition and nonlinear feature modeling, have enabled the automated extraction of core elements such as symbols, annotations, and connecting lines from piping and instrumentation diagrams, the reconstruction of process topologies, and the establishment of semantically enriched knowledge models. These developments provide a foundational framework for high-level applications including automated compliance checking, intelligent piping and instrumentation diagram generation, hazard and operability analysis, and digital twin development. This paper provides a systematic review of the state-of-the-art artificial intelligence-driven methodologies across the ‘perception-cognition-application’ pipeline, analyzes current technical bottlenecks, and outlines future directions.

Santalene and santalol, the primary active components of sandalwood essential oils, possess excellent pharmacological properties, including neurosedative, antibacterial, and anti-inflammatory activities. Owing to the limited supply of sandalwood, heterologous biosynthesis of santalene and santalol has garnered extensive attention. With the rapid advancements in synthetic biology, microbial cell factories have emerged as promising and sustainable methods for production of santalene and santalol. This review summarizes the advances in mining, functional expression, structural characterization, and catalytic mechanisms of two key enzymes in the pathway for production of santalene and santalol: santalene synthase and oxygenase. We have also elucidated metabolic engineering strategies across diverse microbial and plant chassis species, including Escherichia coli, Saccharomyces cerevisiae, and tobacco. Furthermore, we have critically analyzed the current bottlenecks that limit the industrial application of santalene and santalol biosynthesis and identified future directions that may address these problems.

To address the limitations of existing methods in capturing long-term temporal dependencies, local capacity regeneration, and nonlinear degradation characteristics in lithium-ion battery remaining useful life prediction, this paper proposes a coordinate-aware Mamba2 framework based on state-space modeling. Mamba2 is adopted as the backbone to model long-range degradation evolution, while a coordinate feature attention network is introduced in the feature extraction stage to enhance the selection and representation of key degradation information. Additionally, a swiGLU-gated residual network is constructed for feature transformation and prediction, improving the modeling of complex dynamic relationships and endpoint stability. Through the collaborative design of these modules, the proposed framework effectively captures both global degradation trends and local fluctuation patterns. Experiments were conducted on the National Aeronautics and Space Administration, Tongji University, and Xi’an Jiaotong University datasets under single-variable input, multi-variable input, and cross-dataset generalization settings. The mean absolute error values are lower than 0.0098, 0.0016, and 0.0083, while the root mean square error values are lower than 0.0172, 0.0024, and 0.0111, respectively. Results demonstrate that coordinate-aware Mamba2 achieves superior accuracy, lower computational cost, faster training and inference, and stronger robustness to different prediction starting points.

Electrochemical upgrading of polyethylene terephthalate (PET) waste into value-added chemicals offers a promising route for plastic recycling. However, efficient oxidation of PET-derived ethylene glycol (EG) still requires low-cost catalysts with high activity and selectivity. Herein, a NiCo₂O₄ nanoneedle array grown on nickel foam (NiCo₂O₄/NF) was fabricated via a hydrothermal-annealing process and employed as an anode for EG electrooxidation. The NiCo₂O₄/NF electrode delivers 100 mA∙cm^–2 at 1.37 V (vs. RHE) and achieves a formate Faradaic efficiency of approximately 97.0% at 1.50 V (vs. RHE). It also retains 92.7% of its initial current after 50 h of continuous electrolysis. X-ray photoelectron spectroscopy analysis suggests electronic modulation around Ni and Co centers, and combined with electrochemical analyses, these results indicate that the Ni-Co interaction in the spinel framework may contribute to strengthened interfacial EG adsorption and promoted EG oxidation kinetics. Mechanistic studies further identify electrogenerated high-valent Ni/Co oxyhydroxides as the key redox-active species, with formate generated via oxygenated C₂ intermediates followed by C–C bond cleavage. Electrolysis using EG derived from real PET hydrolysates further demonstrates the conversion of PET powder into potassium diformate and terephthalic acid, underscoring the practical potential of this strategy for PET upcycling.