Search Results (96)

Here, we report a facile, reproducible, fully aqueous route to fabricate citric acid–crosslinked poly(vinyl alcohol) (PVA) composite hydrogels incorporating polyaniline nanoparticles (PANI-NP) of ca. 200 nm mean diameter and polydispersity index (PDI) below 0.2, synthesized directly in water. Nanocomposites incorporating 2, 3, and 5% w/w PANI-NP were thoroughly characterized in terms of thickness (obtaining materials of approximately 500 µm), morphology, spectroscopic and thermal properties, surface properties, swelling behavior, and nanomechanical behavior assessed by atomic force microscopy (AFM) operating in Peak Force Quantitative Nanomechanical Mapping (PF-QNM) mode. Incorporation of PANI-NP progressively increased the elastic modulus of the composites (from 794 MPa for neat PVA to values exceeding several GPa at 3–5% w/w loading) and modified swelling capacity to values as low as 140% (from 247% for neat PVA), reflecting nanoscale interfacial interactions. Notably, the hydrogel composites exhibited significant photothermal activity under low-power near-infrared (NIR) LED irradiation (850 nm, 90 mW cm⁻²), achieving temperature increases of up to 13.7 °C even at low PANI-NP loadings, with a stable and reproducible response across multiple heating–cooling cycles. Overall, this work establishes a straightforward, water-based fabrication platform for structurally stable, photothermally active nanocomposites with promising potential in light-responsive smart material applications. Full article

Infrared cameras used in radio telescopes often suffer image degradation in complex optical and thermal environments. Solar radiation, convergent reflected light, and thermal emission from support structures can substantially impair imaging performance. To address this problem, this paper proposes a split reflective ellipsoidal baffle for suppressing infrared imaging degradation. Unlike conventional baffles, which mainly rely on structural occlusion and surface absorption, the proposed design functions as an upstream stray light regulation unit. It also establishes a computational framework integrating ellipsoidal vane geometry, realistic edge microtopography modelling, ray-tracing simulation, and detector plane irradiance response analysis. First, the reflective properties of the ellipsoidal surface are used to construct an off-axis stray light propagation constraint model. Under this model, incident stray radiation is redirected away from the effective imaging path or guided into light-trapping regions between adjacent vanes. Second, a laser confocal microscope is used to capture the true three-dimensional edge morphology of vanes with different materials and machining angles. This strategy addresses the limitations of the conventional 0.02 mm rounded edge approximation, which cannot accurately represent real scattering behaviour. The measured morphologies are then converted into high-fidelity computational models compatible with ray-tracing analysis. Furthermore, stray light suppression performance is evaluated using point source transmittance, detector plane irradiance distribution, and grey scale response in experimental images. Simulation and darkroom experiments show that the proposed baffle suppresses residual stray light more effectively than conventional absorptive baffles. The results demonstrate a computable, manufacturable, and experimentally verifiable strategy for front-end stray light control and baffle optimisation. This strategy can also support image quality enhancement in infrared imaging systems operating under complex optical and thermal environments. Full article

In low-light, occluded, and cluttered environments, single-modality RGB detectors are prone to false positives and missed detections. While infrared (IR) imaging provides relatively stable target visibility under poor illumination, it lacks texture and color information and is susceptible to background thermal noise and imaging variations. To address these limitations, this paper proposes an RGB–IR object detection network, named MCF-YOLO, consisting of three core components. First, the Cross-Modal Hierarchical Fusion (CMHF) module performs stage-wise alignment and fusion on multi-scale features, jointly modeling RGB texture details and IR thermal responses to exploit the structural and semantic complementarity between the two modalities. Second, the Soft Attention Regularization based on Attention Prior (SAR-AP) module derives attention priors from IR features to impose soft constraints on cross-modal attention maps. This mechanism helps the network maintain attention on target-relevant regions, thereby suppressing attention drift caused by low-light noise and complex backgrounds. Third, the Small-Object-Sensitive Detection Head (SOS-Head) processes high-resolution features to strengthen the representation of small targets, improving detection capability in long-range and occluded scenarios. In evaluations on two RGB–IR benchmarks—M3FD and VEDAI—MCF-YOLO achieves improvements of 2.7% in mAP@0.5 and 1.1% in mAP@0.5:0.95 on M3FD, and 5.4% and 4.4%, respectively, on VEDAI. These results suggest that consistency-guided cross-modal fusion and high-resolution small-target modeling are beneficial for RGB–IR detection in low-visibility and cluttered scenes. Full article

Offshore wind turbine blades are chronically exposed to complex marine environments with high humidity, salt spray, strong wind, waves, and intense radiation. Under such conditions, blade defects often exhibit small sizes, weak visual features, and heterogeneous visible infrared manifestations. Conventional single-sensor monitoring and empirically weighted fusion methods are insufficient for reliable defect diagnosis and risk grading. To address this problem, this paper proposes a cross-sensor consistency-guided dual-spectrum fusion framework, termed CG-DSF, for offshore wind turbine blade defect diagnosis and risk assessment. First, visible-light images and infrared thermal images are acquired by UAV-mounted imaging sensors, and sensor-specific branches are constructed to extract surface structural features and thermal anomaly responses. Second, visible and infrared features are aligned at the feature token level, and cross-sensor evidence is evaluated for spatial consistency, diagnostic semantic consistency, and anomaly consistency. A reliability-aware fusion strategy is then used to suppress low-quality or conflicting observations and construct a unified defect representation. Finally, a series of representative simulation case studies are carried out to comprehensively assess the overall performance and practical applicability of the constructed model. Experimental results reveal that the proposed framework possesses evident advantages in blade defect identification for offshore wind turbines, offering a feasible solution for advancing proactive and intelligent condition-based operation and maintenance of offshore wind assets in complex marine environments. Full article

Coral reefs support marine biodiversity, fisheries, tourism, and coastal protection, but they are increasingly threatened by environmental stress and bleaching. Satellite-based reef monitoring has mainly relied on thermal metrics, especially Degree Heating Weeks (DHW), to represent bleaching risk. However, thermal exposure alone may not fully describe reef stress in optically complex coastal waters, where light availability, water clarity, and water-quality conditions can modify coral response. This limitation is important in Indonesia, where reefs span diverse coastal environments and many bleaching observations occur under relatively low DHW. In this study, we develop the Coral Reef Environmental Stress Index (CRESI), implemented as CRESI-Mamba, to estimate coral reef stress in Indonesia as a continuous and interpretable satellite-based stress index. CRESI-Mamba uses 26-week sequences of thermal variables from NOAA Coral Reef Watch and ocean-color variables from NASA Visible Infrared Imaging Radiometer Suite (VIIRS). The model decomposes the inferred stress into thermal, optical, and water-quality pathways, and maps the resulting stress index to bleaching probability for event-based evaluation. CRESI-Mamba was trained and evaluated using 8424 reef observations from eight Indonesian regions. In Leave-One-Region-Out cross-validation (LORO-CV), the model achieved a mean area under the receiver operating characteristic curve (AUC) of $0.795\pm 0.087$. In grouped 5-fold cross-validation, it achieved an AUC of $0.802\pm 0.024$, exceeding the DHW-only baseline ($0.627\pm 0.021$) and performing comparably to stronger thermal-only models, while providing a pathway-decomposed stress index. The estimated stress index separated bleached and not-bleached observations, with paired stress differences of $0.299\pm 0.098$ in LORO-CV and $0.281\pm 0.032$ in grouped 5-fold CV. Pathway analysis showed that the dominant stress pathway differed among regions, with optical stress dominant in several low-DHW bleaching cases. These results show that reef stress in Indonesia is better represented as a multi-pathway environmental condition than as thermal exposure alone. CRESI-Mamba provides a framework for interpreting satellite environmental histories as reef stress, while retaining bleaching probability as an evaluation output. Full article

Forward road information is essential to improve the safety of next-generation advanced driver assistance systems/automated driving systems. In this study, we developed a noncontact friction estimation system that integrates multivariate information from multiple sensors, including three-dimensional light detection and ranging, millimeter-wave radar, and infrared thermometers. We used the continuous peak μ measured by a proprietary friction measurement trailer as the ground truth which has demonstrated extremely high measurement accuracy (R² = 0.9987) on test road sections and similar surfaces. Through multivariate regression analysis using real road data, including snowy surfaces, the system achieved a high explanatory power with an adjusted coefficient of determination of 0.75. In addition, a time-series analysis of squared errors revealed that sensor fusion based on three physical factors, road roughness, moisture content, and thermal response resulted in the most accurate and robust estimation model. Full article

Coloured and variegated leaves are common in shade-tolerant ornamentals. However, it remains unclear whether their photosynthetic performance is determined mainly by pigment abundance or by the organisation of chloroplasts and thylakoids. We tested this in three Epipremnum aureum phenotypes (‘Neon’, ‘Golden’ and ‘Jade’) that share a genetic background but contrast in leaf colour, chloroplast density and thylakoid membrane abundance. Plants were grown in a greenhouse and assessed by hyperspectral and thermal imaging, infrared gas exchange analysis, chlorophyll a fluorescence measurements, and structural, ultrastructural and biochemical analyses. Traits were integrated by principal component analysis, with the quantum yield of CO₂ assimilation per absorbed photon (αCO_2,abs) as the response variable. ‘Neon’ leaves had high specific leaf area and approximately 55% lower maximum Rubisco carboxylation (Vc_MAX) and electron transport capacity (J_MAX) than ‘Jade’, as well as reduced chloroplast and thylakoid abundance and warmer canopies, despite carotenoid enrichment. JIP-test parameters and fluorescence light–response curves showed high absorption and dissipation per PSII reaction centre, elevated excitation pressure, modest non-photochemical quenching (NPQ), low αCO_2,abs, small carbohydrate pools and low intrinsic water-use efficiency. ‘Jade’ leaves developed thick mesophyll with dense chloroplast populations, extensive thylakoid networks, highest NPQ, cool canopies and large carbohydrate reserves, whereas ‘Golden’ leaves combined thin laminae and intermediate chloroplast–thylakoid organisation with early light saturation of CO₂ assimilation and the highest intrinsic water-use efficiency. Principal component analysis revealed a structural axis of chloroplast and thylakoid organisation that better predicted αCO_2,abs, net carbon gain and canopy temperature than pigment abundance. In variegated E. aureum, ‘photon economy’ is therefore governed primarily by chloroplast and thylakoid membrane organisation and abundance rather than by carotenoid accumulation. Full article

Photothermal therapy (PTT) is an emerging minimally invasive approach for cancer treatment that relies on photothermal agents capable of efficiently converting near-infrared (NIR) light into localized heat. In this work, silica–gold nanostructures (SGNs) were synthesized and systematically evaluated to investigate how silica core size influences the photothermal response of the SGNs and optimize their performance as a photothermal agent. SGNs were synthesized with silica cores ranging from 54 to 244 nm in diameter and coated with gold nanoparticles of 4–10 nm in size, enabling controlled tuning of their localized surface plasmon resonance within the NIR region. The morphology and composition were characterized by SEM, TEM, and EDS; optical properties were analyzed by UV-Vis spectroscopy. The SGNs photothermal response low-power laser irradiation at 852 nm and 1310 nm and temperature changes were monitored using a thermographic camera. A maximum temperature increase of 7.1 °C was observed for SGNs with a silica core diameter of approximately 77 nm under the 852 nm laser irradiation. Numerical simulations of the absorption efficiency showed good agreement with experimental UV–Vis spectra and thermal measurements, revealing a size-dependent shift of the absorption toward longer wavelengths for larger nanostructures. These results demonstrate that the photothermal response of silica–gold nanostructures can be rationally tuned through the control of core size and gold growth parameters, providing a framework for the design of wavelength-matched photothermal agents for PTT applications. Full article

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

Lipoxygenases are enzymes found in plants, mammals, and other organisms that catalyse the hydroperoxidation of polyunsaturated fatty acids, such as arachidonic, linoleic, and linolenic acids. They have attracted a lot of attention as molecular targets for industrial and biomedical applications, due to their implication in key biological processes, such as plant development and defence, cell growth, as well as immune response and inflammation. Soybean (Glycine max) lipoxygenase (LOX) is a versatile biocatalyst used in biotechnology, pharmaceutical, and food industries. sLOX1, a soybean LOX isoform, is central in various industrial applications; thus, it is of particular interest to develop an efficient sLOX1 isolation process, control its activity, and leverage its potential as an effective industrial biocatalyst, tailoring it to a specific desired outcome. In this study, sLOX1 was extracted and purified from soybean seeds using an optimized protocol that yielded an enzyme preparation with higher activity compared to the commercially available lipoxygenase. Comprehensive biophysical characterization employing dynamic and electrophoretic light scattering, fluorescence, and Fourier-transform infrared spectroscopies revealed that sLOX1 exhibits remarkable structural and functional stability, particularly in sodium borate buffer (pH 9), where it retains activity and integrity up to at least 55 °C and displays minimal aggregation under thermal, ionic, and temporal stress. In contrast, sLOX1 in sodium phosphate buffer (pH 6.8) remained relatively stable against ionic strength and time but showed thermally induced aggregation above 55 °C, while in sodium acetate buffer (pH 4.6), the enzyme exhibited a pronounced aggregation tendency under all tested conditions. Overall, this study provides physicochemical and stability assessments of sLOX1. The combination of enhanced catalytic activity, high purity, and well-defined stability profile across diverse buffer systems highlights sLOX1 as a promising and adaptable biocatalyst for industrial applications, offering valuable insights into optimizing lipoxygenase-based bioprocesses. Full article

Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality assessment system was proposed using spatiotemporal domain analysis with long-wave infrared (LWIR)-based thermal diffusion phenomics, enabling non-destructive prediction of the internal Brix of apples during transport. After cooling, the thermal gradient of the apple surface during the cooling-to-equilibrium interval was extracted. This gradient was used as an input variable for multiple linear regression, Ridge, and Lasso models, and the prediction performance was assessed. Overall, 492 specimens of 5 cultivars of apple (Hongro, Arisoo, Sinano Gold, Stored Fuji, and Fuji) were included in the experiment. The thermal diffusion response of each specimen was imaged at a sampling frequency of 8.9 Hz using LWIR-based thermal imaging, and the temperature changes over time were compared. In cross-validation of the integrated model for all cultivars, the coefficient of determination (R²_cv) was 0.80, and the RMSEcv was 0.86 °Brix, demonstrating stable prediction accuracy within ±1 °Brix. In terms of cultivar, Arisoo (Cultivar 2) and Fuji (Cultivar 5) showed high prediction reliability (R²_cv = 0.74–0.77), while Hongro (Cultivar 1) and Stored Fuji (Cultivar 4) showed relatively weak correlations. This is thought to be due to differences in thermal diffusion characteristics between cultivars, depending on their tissue density and water content. The LWIR-based thermal diffusion analysis presented in this study is less sensitive to changes in reflectance and illuminance compared to conventional NIR and visible light spectrophotometry, as it enables real-time measurements during transport without requiring a separate light source. Surface heat distribution phenomics due to external heat sources serves as an index that proximally reflects changes in the internal Brix of apples. Later, this could be developed into a reliable commercial screening system to obtain extensive data accounting for diversity between cultivars and to elucidate the effects of interference using external environmental factors. Full article

External insulation of coastal power grids transmitting offshore wind power faces significant threats from salt fog flashovers. Current arc monitoring and early warning technologies for flashover are severely inadequate. Research on salt fog discharge processes and determining the threshold at the flashover brink for transmission equipment external insulation is crucial for ensuring the safe operation of coastal grids delivering offshore wind power. Fiber Bragg Grating (FBG), with its advantages of compact size, excellent insulation, and fast response, enables effective discharge monitoring and identification of the critical flashover state on external insulation surfaces. In this study, FBGs were embedded at the interfaces of typical external insulation specimens, including silicone rubber plates and epoxy resin plates, to conduct contaminated AC salt fog discharge tests. Synchronized measurements of visible light images, infrared thermal images, and FBG interface temperature were conducted to investigate the discharge physical processes on silicone rubber insulating surfaces and the flashover threshold based on FBG temperature rise rate. The results indicate that discharge process can be divided into three phases: arc initiation, extension, and flashover based on the characteristics of arc visible light images. By comparing arc locations in infrared and visible light images with the corresponding FBG interface temperature rise, the arc phase criterion of FBG interface temperature rise rate and position were proposed. Furthermore, through multiple experiments, it has been found that flashover occurs when both interface temperatures reached above 4.6 × 10⁻² °C/s. This study provides a novel research methodology for physical process of external insulation discharge and flashover warning in coastal salt fog environments. Full article

Addressing crop responses to drought and nitrogen stress is crucial for improving resilience and ensuring sustainable agriculture under changing climatic conditions. This study investigates the physiological responses of wheat (Videodur [DU], Sensas [SW]) and barley (Tiroler Imperial [SG1], Amidala [SG2]) cultivars to drought and nitrogen stress during early reproductive to full maturity stages (BBCH 70 to 90) using infrared (IR) and visible near-infrared–shortwave infrared (VNIR-SWIR) hyperspectral imaging. Evapotranspiration (ET) and respiration were analyzed as functions of mean plant temperature (T_plant), light intensity, plant water status (indicated by the Normalized Difference Water Index, NDWI), and air humidity. Results revealed that drought stress significantly reduced NDWI and ET while increasing T_plant, with wheat cultivars showing greater sensitivity to water deficit. Barley, particularly SG2, exhibited superior water retention and thermal regulation, highlighting its potential for drought resilience with consistently higher NDWI values and lower T_plant. Temporal analysis identified the reproductive stage as the most vulnerable to stress, with a sharp decline in NDWI and rise in T_plant, emphasizing the need for stage-specific interventions. Regression models explained 74% of ET variance and 67% of respiration variance, underscoring the predictive power of NDWI and T_plant as proxies for plant water status and metabolic activity. Real-time evapotranspiration (ET) measurements using a balance during precision watering further validated the predictive capabilities of NDWI and T_plant. These findings provide valuable insights into growth stage-specific breeding programs and sustainable crop management strategies under environmental stress conditions. Full article

In response to problems such as large target scale variations, strong background noise, and blurred features leading by low contrast in infrared target detection in near space environments, this paper proposes an efficient detection model, YOLO-MARS, which is based on YOLOv8. The model introduces a Space-to-Depth (SPD) convolution module into the backbone section, which retains the detailed features of smaller targets by downsampling operations without information loss, alleviating the loss of the target feature caused by traditional downsampling. The Grouped Multi-Head Self-Attention (GMHSA) module is added after the backbone’s SPPF module to improve cross-scale global modeling capabilities for target area feature responses while suppressing complex thermal noise background interference. In addition, a Light Adaptive Spatial Feature Fusion (LASFF) detector head is designed to mitigate the scale sensitivity issue of infrared targets (especially smaller targets) in the feature pyramid. It uses a shared weighting mechanism to achieve adaptive fusion of multi-scale features, reducing computational complexity while improving target localization and classification accuracy. To address the extreme scarcity of near space data, we integrated 284 near space images with the HIT-UAV dataset through physical equivalence analysis (atmospheric transmittance, contrast, and signal-to-noise ratio) to construct the NS-HIT dataset. The experimental results show that ${mAP}_{@0.5}$ increases by 5.4% and the number of parameters only increase 10% using YOLO-MARS compared to YOLOv8. YOLO-MARS improves the accuracy of detection significantly while considering the requirements of model complexity, which provides an efficient and reliable solution for applications in near space infrared target detection. Full article

Background/Objectives: The rapid emergence of multidrug-resistant bacterial infections demands innovative non-antibiotic therapeutic strategies. Dual-modal photoresponse therapy integrating photodynamic (PDT) and photothermal (PTT) effects offers a promising rapid antibacterial approach, yet designing single-material systems with synergistic enhancement remains challenging. This study aims to develop uniform Cu-based metal–organic framework micrometer cubes (Cu-BN) for efficient PDT/PTT synergy. Methods: Cu-BN cubes were synthesized via a one-step hydrothermal method using Cu(NO₃)₂ and 2-amino-p-benzoic acid. The material’s dual-mode responsiveness to visible light (420 nm) and near-infrared light (808 nm) was characterized through UV–Vis spectroscopy, photothermal profiling, and reactive oxygen species (ROS) generation assays. Antibacterial efficacy against multidrug-resistant Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) was quantified via colony counting under dual-light irradiation. Results: Under synergistic 420 + 808 nm irradiation for 15 min, Cu-BN (200 μg/mL) achieved rapid eradication of multidrug-resistant E. coli (99.94%) and S. aureus (99.83%). The material reached 58.6 °C under dual-light exposure, significantly exceeding single-light performance. Photodynamic analysis confirmed a 78.7% singlet oxygen (¹O₂) conversion rate. This enhancement stems from PTT-induced membrane permeabilization accelerating ROS diffusion, while PDT-generated ROS sensitized bacteria to thermal damage. Conclusions: This integrated design enables spatiotemporal PDT/PTT synergy within a single Cu-BN system, establishing a new paradigm for rapid-acting, broad-spectrum non-antibiotic antimicrobials. The work provides critical insights for developing light-responsive biomaterials against drug-resistant infections. Full article