This study proposes a condition-based maintenance monitoring method based on Geometry-based Optical Focus Metrology (GOFM) to detect wafer table edge deterioration early and enable proactive interventions before actual Critical Dimension (CD) bridge defects occur. In advanced Deep Ultraviolet (DUV) immersion photolithography, prolonged equipment operation mechanically wears the wafer table, inducing Edge-Roll-Off (ERO). Because conventional optical metrology struggles to separate this localized defocus from process noise, this work utilizes the existing GOFM technique to isolate the pure focus residual within the 140-147 mm radius region. To quantify this hardware-specific degradation, a mathematical dual-indicator system was constructed. This framework integrates a statistical threshold, the Range Percentile 97%, to reject baseline measurement noise, and a geometric variable, Slope × 3, to capture the topographical drop in the outermost 3 mm. Analysis of long-term time-series data from multiple High-Volume Manufacturing (HVM) scanners confirmed a strong correlation (R2=0.93) between these indicators. Furthermore, we proved that the drift trajectory of Slope × 3 deterministically predicts mechanical failure prior to defect occurrence on production wafers. Based on these findings, an automated condition-based maintenance architecture was designed using an OR-logic decision gate. By triggering a preemptive table replacement at a quality-based critical warning threshold, this system converts routine time-based scheduling into a data-driven paradigm, maximizing both edge yield and equipment uptime. Furthermore, this proposed framework establishes a solid foundation for future extensions toward machine learning-based predictive maintenance.
This work studies trustworthy use of large language models for remote sensing satellite downlink scheduling. Rather than accepting a generated optimization model at face value, we organize the workflow into three guarded steps: candidate generation, benchmark-based validation, and fallback exact solving. The core technical component is a global time-slicing validator that converts visibility windows into atomic intervals; so, mutual exclusion at the ground-station side, mutual exclusion at the satellite side, and per-satellite download caps can be checked in a physically faithful manner. Results on a prototype instance indicate that LLM-based modeling can be integrated into a dependable scheduling pipeline when external verification and recovery are built into the loop.
Background: Cutaneous melanoma (CM) is a highly immunogenic malignant neoplasm. It features high mutational burden and intense lymphocytic infiltration, supporting the use of immunotherapies, especially inhibitors of the programmed cell death protein 1 (PD-1) checkpoint. Despite advances with anti-PD-1 therapies, such as nivolumab and pembrolizumab, many patients still experience resistance. This result highlights additional immunosuppressive mechanisms within the tumor microenvironment (TME) that limit T-lymphocyte-mediated responses. Objectives: The aim was to discuss the immunologic and metabolic bases of PD-1- and CD73-mediated pathways and evidence that CD73 inhibition can boost PD-1 inhibitor efficacy by acting on convergent immunosuppressive pathways. Methods: We conducted a narrative literature review focusing on tumor immunosuppression, purinergic signaling and checkpoint inhibitor-based immunotherapy. Results: The purinergic pathway, mediated by the ectonucleotidase CD73, is a critical regulator of tumor immunosuppression. CD73 converts extracellular adenosine monophosphate (AMP) into adenosine. This adenosine accumulates in the hypoxic and inflamed TME, exerting immunosuppressive effects. Adenosine acts as a "metabolic brake," inhibiting proliferation, cytokine production, and cytotoxic activity of CD8+ T lymphocytes and natural killer (NK) cells. It also promotes the expansion of regulatory T cells (Tregs) and tumor progression. This axis may limit responses to PD-1 blockade, suggesting that complementary pathways are active. Conclusions: Integration of PD-1 and CD73 pathways suggests that CD73 inhibition may enhance PD-1 blockade by targeting convergent immunosuppressive mechanisms. This supports the exploration of combination strategies to broaden the benefits of immunotherapy in CM.
Background: Neuroinflammation and gut-brain axis (GBX) dysregulation are key pathological drivers of stress-related neuropsychiatric disorders. Zhi-Zi-Chi Decoction (ZZCD), a classic Traditional Chinese Medicine (TCM) formula, has been clinically used to alleviate mental disturbances via the TCM principle of "clearing heat and relieving restlessness." Still, its modern neuroprotective mechanisms, especially its links to gut microbiota and central signaling pathways, remain incompletely elucidated. Purpose: This study aimed to systematically investigate the therapeutic effects of ZZCD on chronic restraint stress (CRS)-induced neurodysfunction in mice and clarify its mechanisms from the perspectives of TCM theory, material basis, gut microbiota-metabolite axis, and central signaling pathways. Method: CRS mice were treated with ZZCD or protocatechuic acid. Behavioral tests evaluated depression- and anxiety-like behaviors. UHPLC-Q-TOF/MS identified ZZCD's chemical constituents; 16S rRNA sequencing and untargeted metabolomics analyzed gut microbiota and metabolite changes. Western blot, immunofluorescence, and proteomics examined neuroinflammation, microglial polarization, and signaling pathway activity (PI3K/Akt/mTOR, AMPK). Results: ZZCD reversed CRS-induced depression- and anxiety-like behaviors and suppressed neuroinflammation. Mechanistically, UHPLC-Q-TOF/MS identified 424 ZZCD constituents, with prenol lipids, organooxygen compounds, and flavonoids as the most abundant. ZZCD reversed CRS-induced imbalance in gut microbiota, reducing pro-inflammatory Prevotella and enriching beneficial Lactobacillus, and mediated the enrichment of the prebiotic metabolite PCA in colonic and serum samples, which crossed the blood-brain barrier (BBB) to exert neuroprotection. Additionally, ZZCD and PCA normalized the PI3K/Akt/mTOR pathway and activated AMPK, promoting M2 microglial polarization and restoring synaptic plasticity. Conclusions: ZZCD exerts antidepressant effects by a gut-microbiota-dependent modulation of PCA-PI3K/Akt/mTOR and AMPK dual axes that converts microglia from M1 to M2, providing ethnopharmacological evidence and a mechanistic rationale for its clinical application in major depressive disorder.
Massive Internet of Things (IoT) and sensor-network services in 5G/6G systems increasingly rely on network slicing to support large-scale sensing, monitoring, and mission-critical applications. In such sliced infrastructures, Proportional Fair (PF) allocation assigns resources according to slice-reported demands. This reliance on trusted demand reporting makes coexisting slices, including mMTC-based IoT sensor slices, vulnerable to resource exhaustion attacks, where a malicious slice inflates its demand to monopolize shared resources and induce Service Level Agreement (SLA) violations. Existing unsupervised defenses mainly focus on anomaly detection, while the translation of detection results into resource-level mitigation remains insufficiently addressed. To bridge this gap, this paper proposes AutoGuard-Hybrid, an unsupervised detection-to-mitigation framework that combines complementary anomaly detectors with allocation-aware mitigation policies to preserve slice-level service availability. Unlike prior detection-only approaches, AutoGuard-Hybrid converts unsupervised anomaly evidence into allocation-aware demand purification before PF scheduling. Its key design is a closed-loop integration of Isolation Forest (IF) and Long Short-Term Memory Autoencoder (LSTM-AE) as spatial and temporal front-end detectors with Adaptive Clipping and a Safety Cap, which translate anomaly scores into demand purification actions. Experiments show that AutoGuard-Hybrid remains comparable to Isolation Forest under Continuous attacks and improves the mean system-wide SLA violation rate by 27.6% under Adaptive Probing attacks. Stage activation analysis further shows that LSTM-AE activations increase from 9.3 under Continuous attacks to 29.4 under Adaptive Probing attacks. Ablation results show that Adaptive Clipping alone reduces the system-wide SLA violation rate by 75.0%, while the full mitigation pipeline achieves an 84.6% total reduction. AutoGuard-Hybrid operates within the 1 ms Transmission Time Interval (TTI) constraint and provides a practical defense framework for next-generation network slicing-enabled IoT and sensor-network services.
Understanding hand-paddle interaction is essential for optimizing performance and preventing injury in kayaking, yet coaches still lack objective, practical tools. We present a soft, instrumented glove that measures and dynamically maps palmar pressure throughout the stroke cycle. A matrix of piezoresistive sensors is integrated into the glove and connected to dedicated electronics housed in a waterproof enclosure. A viscoelastic model converts sensor resistance into forces, enabling time-resolved 3D mapping of contact mechanics. Data are transmitted via Bluetooth Low Energy (BLE). Experimental validation on a kayak ergometer across multiple cadences demonstrated accurate measurements (per-sensor root mean square error (RMSE) of ±2 N), clear delineation of pull and push phases, evolving pressure distribution over the motion, and a peak total right-hand force of 186 N at high cadence. Beyond feasibility, these results position the glove as a practical training aid: it supports athlete-specific load monitoring and the early detection of potentially problematic movement patterns.
Advanced nanoimprint lithography (NIL) is promising for inorganic semiconductor patterning because it enables high-resolution replication with a relatively simple process flow; however, yield loss increasingly originates from spatially distributed, subcritical distortions accumulated across coating, exposure, etching, and imprinting. In this study, we propose an integrated physics-based and data-driven framework for pre-manufacturing defect-risk prediction in NIL. The framework combines an NDA-safe layout database, a physics-based process twin, and a stochastic risk prediction model using a physics-augmented convolutional neural network with conformal uncertainty calibration. Starting from binary design layouts, the process twin sequentially captures resist thickness variations during spin coating, proximity-induced dose redistribution and development-induced pattern deformation during electron-beam lithography (EBL), density-sensitive pattern transfer during reactive ion etching (RIE), and three-dimensional resist filling during imprinting, thereby generating physically consistent parameter maps for downstream learning. The results demonstrate an end-to-end virtual inspection flow that converts layouts into spatially resolved risk maps before fabrication. In addition, patterns with similar contour extent but different local density exhibit distinctly different risk distributions, indicating that manufacturability is governed not only by nominal geometry but also by local pattern environment. These findings support pre-manufacturing virtual inspection as a physically interpretable route for early yield-risk screening in advanced NIL.
Accurate viral protein function annotation is essential for genomic surveillance, yet conventional retrieval-augmented generation (RAG) pipelines often fragment biological evidence into fixed-length text chunks, disrupting relationships among ORFs, annotations, structural domains, sequence motifs, residue mappings, and model-derived attention evidence. We propose ViroBioTree, a tree-structured biological evidence retrieval framework for downstream viral protein evidence review rather than a new primary annotation classifier. Built as an evidence organization layer on ViralMultiNet-derived ORF-level predictions and annotations, ViroBioTree converts sequence, annotation, structure, and attention evidence into typed biological nodes and traceable edges, then performs deterministic multi-channel recall, evidence-aware reranking, balanced TopK selection, rule-based verification, and node-cited report generation. In a demo benchmark, ViroBioTree achieved its strongest deterministic proxy performance on structure-explanation tasks, with Precision@K = 1.0, Recall@K = 1.0, and diversity = 0.52; these values reflect expected node-type and tag agreement rather than independent biological correctness. A bounded full-scale SARS-CoV-2 index contained 39,800 ORF rows, 80,000 attention records, 199,418 nodes, and 495,886 edges. In a stratified full20k diagnostic evaluation, ViroBioTree showed task-dependent advantages over LlamaIndex vector retrieval for conflict detection, evidence retrieval, and structure explanation, while LlamaIndex remained competitive or stronger for annotation-rich function annotation. A cross-family Influenza A Virus (IAV) diagnostic audit showed that the schema can represent IAV evidence namespaces while explicitly exposing missing formal ORF inputs, missing attention evidence, and unavailable residue/PDB assertions. Supplementary robustness, external sanity-check, diversity-risk, expert-evaluation, domain-tool positioning, and cross-family audit analyses supported traceability, report quality, and conservative evidence handling, but also showed that stable Precision@K under query perturbation does not necessarily imply stable retrieved evidence sets. ViroBioTree operates offline and deterministically, but does not address raw-read assembly, base calling, primary ORF prediction, or wet-lab validation. Its results should be interpreted as proxy and expert-reviewed evidence for traceable viral protein evidence retrieval and report generation rather than as direct validation of biological function annotation.
Achieving durable oxygen evolution reaction (OER) under industrial alkaline water electrolysis (AWE) conditions remains a formidable challenge, arising from dynamic Fe dissolution and segregation in NiFe layered double hydroxides (LDH) and further aggravated by sluggish ion transport and bubble release at the gas-liquid-solid interface. Herein, we report a design concept of using an ultrathin sodium polyacrylate (PANa) hydrogel layer on NiFe LDH as a spatially confining, transport-permissive interphase to dynamically stabilize Fe sites and enhance multiphase interfacial transport for boosting OER durability in hectowatt-scale AWE. We demonstrate that the PANa interphase converts uncontrolled Fe dissolution-segregation into an interfacially confined and self-regulated dissolution-redeposition process, in which carboxylate-mediated Fe-O-C coordination thermodynamically stabilizes lattice Fe by suppressing overoxidation, while the hydrated polymer network kinetically retains transiently dissolved Fe within the interfacial region and favors its reincorporation toward homogeneous active-phase regeneration. Meanwhile, the carboxylate-rich framework reorganizes the interfacial hydrogen-bond network to accelerate OH- transport and promote rapid O2 disengagement through its porous superaerophobic architecture. In an industrial 600 W-scale alkaline electrolyzer (679 cm2 total anode area), the PANa/NiFe LDH anode sustains stable operation for over 2500 h at 0.5 A cm-2, with an energy consumption as low as 4.25 kWh Nm-3 H2 and a competitively low projected hydrogen production cost of US$ 2.38 kgH2-1.
The synthesis of reactive sucrose derivatives is of significant interest for the development of novel biocompatible polymers. In this study, an octa-substituted sucrose derivative containing isocyanate groups was synthesized via a urethane-forming reaction carried out in an aprotic solvent at the phase interface. This approach exhibits high selectivity and provides a target product yield of up to 60%. Subsequently, using the same reaction mechanism, the isocyanate derivative was converted into an octa-functional methacrylate derivative capable of forming three-dimensional cross-linked networks. The structures of both the intermediate and final products were confirmed by IR, 1H NMR, and mass spectrometry. The sucrose-based prepolymer was further evaluated in the formation of cross-linked structures for potential application as bone-substituting implants. Using various photocuring techniques, including two-photon 3D printing, both plates and microstructured scaffolds were fabricated. These structures exhibited high thermal stability, elastic properties comparable to those of bone tissue, and no toxic effects on cells.
Visual response to anti-vascular endothelial growth factor (anti-VEGF) therapy in macular edema secondary to retinal vein occlusion (RVO-ME) varies among patients, and baseline predictors remain incompletely defined. This study investigated baseline predictors of visual response and treatment burden after anti-VEGF therapy in eyes with RVO-ME. This retrospective observational study included 80 eyes from 80 patients with RVO-ME treated at a single center. Baseline clinical characteristics, optical coherence tomography (OCT) biomarkers, and hematologic inflammatory indices were collected. Decimal visual acuity was converted to approximate Early Treatment Diabetic Retinopathy Study (ETDRS) letter scores. Poor functional response was defined as a gain of ≤10 ETDRS letters at 1 month after the third anti-VEGF injection. A composite endpoint was used for sensitivity analysis. Treatment burden was assessed by the number of injections within 6 months. Mean baseline and final ETDRS letter scores were 37.46 and 58.93, respectively, with a mean gain of 21.47 letters. Poor functional response occurred in 21 eyes (26.3%). After excluding sparse HRVO cases from regression modeling, CRVO was associated with poor functional response compared with BRVO (OR 6.81, 95% CI 1.17-39.74, p = 0.033), as were higher baseline ETDRS letter score per 10-letter increase (OR 1.85, 95% CI 1.20-2.84, p = 0.005) and male sex (OR 5.46, 95% CI 1.34-22.35, p = 0.018). Poor response was less frequent among patients with hypertension (OR 0.13, 95% CI 0.03-0.59, p = 0.008). Firth penalized logistic regression yielded similar results. In the continuous outcome model, CRVO was associated with lower final ETDRS letter score (β = -13.78 letters, 95% CI -19.62 to -7.93, p < 0.001). Conventional qualitative OCT biomarkers were not significantly associated with the primary gain-based endpoint, although CST showed stronger relevance in the composite endpoint analysis. In adjusted Poisson regression, CRVO showed a non-significant trend toward higher injection count. In this small retrospective RVO-ME cohort treated with ranibizumab, CRVO was consistently associated with poorer short-term visual outcome. The association between baseline visual acuity and gain-based response appeared to be influenced by a ceiling effect. Conventional qualitative OCT biomarkers and hematologic inflammatory indices showed limited but potentially complementary value, and treatment-burden findings should be interpreted as exploratory.
This work presents an enhanced photomechanical optical sensor inspired by our previously reported bio-inspired uncooled infrared detector. Performance improvement is achieved by strengthening the interfacial bond between the photothermal dendrite-polydopamine nanoparticle (PDA NP)/polydimethylsiloxane (PDMS) composite-and the piezoresistive laser-induced nanocarbon film, with a flexible PDMS substrate that provides both thermal insulation and mechanical stability. The resulting sensor exhibits a responsivity of 51.6 W-1 under 808 nm irradiation, an order-of-magnitude enhancement over the unmodified device. Wavelength-dependent characterization (455-1550 nm) shows responsivity decreasing from 93.1 W-1 at 455 nm to 14.4 W-1 at 1550 nm, with response times on the order of seconds across this range. Extending this trend into the longer-wavelength region of blackbody radiation, the mechanism transitions to a predominantly bolometric mode. The device also demonstrates stable detection of several hundred microwatts and robust durability at 455 nm. These results validate interface engineering strategy as a viable pathway toward high-performance uncooled optical detection, advancing bio-inspired detectors from functional mimicry toward an application-ready platform. These findings confirm PDA NPs as effective photothermal converters primarily at shorter wavelengths, while the wavelength-dependent response suggests future tailoring of spectral sensitivity using long-wavelength-absorbing materials.
Flexible strain sensors have attracted considerable attention in gait recognition owing to their ability to adhere directly to the skin near joints and transduce local deformation. In existing work, however, sensor placement and orientation are largely determined by anatomical experience, while multi-channel classification still relies on back-end digital processors, whose power consumption and latency constrain system practicality in wearable scenarios. This paper presents an integrated design path that proceeds from skin-mechanics theory through sensor-layout optimization to analog-domain front-end inference. On the layout side, the lines-of-non-extension (LoNE) theory is employed to convert the selection of sensor attachment angles from empirical judgment into a calculable mechanics problem; guided by the spatial course of LoNE in the ankle and knee regions, the positions and angles of the nine sensors are determined individually-channels perpendicular to the LoNE capture maximum strain, channels offset by 45 degrees supplement non-sagittal-plane information, and a channel aligned along the LoNE provides a near-zero-strain reference. On the circuit side, the mathematical equivalence between the weighted summation of a linear classifier and Kirchhoff's current law (KCL) nodal current superposition is exploited to map the classification operation onto current aggregation in an analog circuit, yielding an in-sensor computing (ISC) front end in which the nine-channel weighted summation is completed in a single analog step. The sensors are fabricated by screen-printing a liquid-metal-polymer composite conductive ink onto a TPU film substrate, with a gauge factor RSD of 6.8% and a tensile linearity R2>0.99. Using walking, running, and stair descent as verification targets, the analog classifier reaches 99% accuracy at the circuit-level functional-verification stage. On real multi-subject data, it achieves 87.0%±8.4% accuracy under intra-subject cross-session validation, with an analog-domain inference response faster than 100μs. This design path is not bound to a specific joint or sensor material; when the layout methodology is extended to additional joint regions and the circuit architecture incorporates multiple outputs to cover more classification categories, the same workflow remains applicable, offering a promising low-power, lightweight technical solution for wearable motion monitoring.
Among diverse industrial wastes, antibiotic fermentation residues containing high concentrations of nosiheptide pose significant environmental and health risks. This study demonstrates that black soldier fly larvae (BSFL) can effectively degrade the nosiheptide residues within this fermentation matrix when blended with potato peel waste. Optimal degradation efficiency was achieved at a dry weight ratio of 3:5 (antibiotic fermentation residue to potato peel waste), yielding a 40.02% material reduction, an 8.63% bioconversion rate, and a 55.74% nosiheptide degradation rate. Further optimization of the larva-to-feed ratio enhanced nosiheptide degradation to 58.21%. Following 48 h of gut emptying period, no detectable nosiheptide remained within the tissues of the treated BSFL. The harvested larvae demonstrated high nutritional value, with crude protein and crude fat contents reaching up to 35.64% and 32.65%, respectively. The larvae also contained a comprehensive profile of essential amino acids, with the glutamic acid content exceeding 3%, which enhances feed palatability. Highly concentrated antibiotic treatments significantly increased the relative abundance of Bacteroidetes within the BSFL gut microbiota, with Dysgonomonas emerging as the dominant genus. This study highlights a novel strategy for degrading residual nosiheptide and converting waste into a valuable protein source, offering an eco-friendly solution for industrial waste management.
[This corrects the article DOI: 10.3389/fimmu.2026.1735785.].
High-temperature temporary sealing operations require liquid plug materials that can be placed as low-viscosity precursors, converted into mechanically stable networks under reservoir temperature, and subsequently removed after service. Existing epoxy-based sealing systems generally provide high post-curing strength, but the coordination among pumpability, thermally triggered curing, and post-service degradability remains insufficiently addressed. In this work, an epoxidized soybean oil (ESO)-modified epoxy-anhydride liquid plug was designed to regulate these sequential stages within a single material system. The precursor formulation, rheological transition, curing kinetics, mechanical response, network structure, and degradation behavior were evaluated using viscosity monitoring, curing-time tests, DSC, compression testing, DMA, gel fraction and swelling measurements, FTIR, and high-temperature degradation experiments. The optimized precursor exhibited an initial viscosity of 65.4 ± 2.1 mPa·s, remaining below the pumpability threshold of 100 mPa·s before curing. Its curing time was adjustable within 1-10 h at 120-140 °C through temperature and initiator regulation. ESO incorporation produced a non-monotonic mechanical response, with the optimized network reaching a compressive strength of 112.5 ± 3.5 MPa and an elastic modulus of 142.50 ± 5.26 MPa. FTIR and thermal-mechanical analyses supported the formation of an ester-rich epoxy-anhydride network containing both rigid epoxy-derived segments and ESO-derived flexible chains. In the post-service stage, degradation was strongly temperature dependent, with the characteristic unsealing time decreasing from 84 h at 120 °C to 24 h at 130 °C and 18 h at 140 °C. The combined results define a coupled curing-degradation window in which pumpable placement, thermal network formation, load-bearing sealing, and controlled unsealing are temporally separated but structurally connected.
Per- and polyfluoroalkyl substances (PFASs) constitute a chemically diverse family of persistent contaminants, the regulation of which is tightening rapidly in Europe and the United States. Granular activated carbon, selective ion exchange, and pressure-driven membranes remove many long-chain PFASs, but their performance is less robust for short-chain and ultrashort species, and all generate concentrated secondary waste streams. Hydrophobic deep eutectic solvents (DESs), including natural deep eutectic solvents (NADESs), have emerged as tunable liquid extractants able to concentrate PFASs into small solvent volumes that can be regenerated or coupled to destruction. This perspective differs from existing DES-PFAS reviews by converting qualitative solvent-selection arguments into a decision framework with explicit acceptance gates: broad PFAS affinity, a component-resolved non-migration specification for treated water, viscosity and mass-transfer limits, regenerability targets, and techno-economic/life-cycle benchmarking against incumbent processes. We refine the bifunctional DES design hypothesis by separating validated regimes from unresolved cases, identifying the reliability limits of COSMO-RS, molecular dynamics, and machine-learning screening, and defining tiered reporting requirements for early-stage studies. The central message is that PFAS-extracting DES should no longer be evaluated only by single-compound removal percentages; they must be judged as integrated, closed-loop treatment materials with solvent losses, regeneration stability, destruction compatibility, cost, and environmental impacts that are quantified from the outset.
Intelligent completion technologies, including Inflow Control Valves (ICVs), have become increasingly important for remotely managing zonal production in complex well architectures. However, quantifying flow rates and phase fractions in such systems remains challenging due to space constraints and the harsh downhole environment, which limit the deployment of conventional sensors. Distributed Acoustic Sensing (DAS) provides a promising solution by converting standard fiber-optic cables into dense arrays of acoustic sensors. While DAS has been successfully applied in applications such as integrity monitoring and leak detection, its use for direct two-phase flow characterization within intelligent completions remains largely unexplored. In this study, we present a DAS-based methodology to monitor and analyze oil-water two-phase flow in horizontal experiments that mimic field conditions. Acoustic data collected from DAS are transformed into time-frequency spectrograms using Short-Time Fourier Transform (STFT) to extract dynamic spectral features. These features are then correlated with pressure drop across the ICV and flow rate, revealing distinct frequency band behaviors associated with fluid changes. To quantify flow characteristics, a power-law model is trained using spectral features to predict flow rate and phase fractions. The results demonstrate strong predictive capability for pressure drop and flow rate under controlled laboratory conditions, highlighting the potential of DAS for multiphase flow diagnostics in field applications with intelligent completions, while water cut prediction remains challenging due to the complex and non-unique relationship between flow conditions and DAS response and is left for future work. This research not only provides new insights into the acoustic response of oil-water flows but also introduces a data-driven framework for leveraging DAS in real-time flow monitoring and control within ICV-equipped completions.
Alkanes are saturated hydrocarbons that serve as available and cost-effective feedstock for producing alkenes, key intermediates in numerous industrial processes. A mutant bacterial strain, Rhodococcus sp. KSM-B-3M, was previously reported to efficiently convert alkanes into alkenes and was later utilized by us to selectively transform linear alkanes into a variety of alkyl derivatives through a two-step process. Here, we explored the biological mechanisms underlying the unique biotransformation capability of strain KSM-B-3M by integrating genomics, transcriptomics, proteomics, and 3D-structural modelling. Strain KSM-B-3M demonstrated downregulation of the fatty acid degradation pathway, lacking the pR8L1 megaplasmid that carries multiple fatty acid degradation genes, accompanied by a parallel high expression of the acyl CoA-desaturase gene. Partial curing of the pR8L1 plasmid from a wild-type (WT) strain conferred the ability to dehydrogenate n-hexadecane to cis-hexadecene. Overexpression of the acyl-CoA desaturase gene similarly induced cis-hexadecene formation in the WT strain, acting cumulatively with fatty acid degradation downregulation. Acyl CoA-desaturase 3-D modeling suggested that the enzyme directly dehydrogenates n-hexadecane to form cis-hexadecene, supporting its direct role in this unique biotransformation. These findings advance our understanding of the mechanism behind this biotransformation, which holds promise for sustainable and cost-effective production of alkyl derivatives.
The semihydrogenation of alkynes is an important and direct synthetic strategy to obtain Z-alkenes. Although great success has been achieved with various transition metal catalysts, a mild and metal-free approach is still highly desired. Herein, we report a metal-free photocatalytic transfer hydrogenation method for the semireduction of alkynes, wherein γ-terpinene acts as the reductant, while both H2O and γ-terpinene serve as hydrogen sources. A range of alkynes can be efficiently converted to Z-alkenes via transfer hydrogenation followed by photoinduced E → Z isomerization. This green and sustainable approach provides a practical alternative to the synthesis of combretastatin A-4.