Search Results (112)

Nano–bio hybrid catalysts have emerged as a promising platform for sustainable energy conversion by integrating the high selectivity of enzymes with the structural robustness and conductivity of nanomaterials. In recent years, the growing demand for clean energy technologies has driven the development of biohybrid systems capable of efficient electron transfer, enhanced catalytic activity, and improved operational stability. This review comprehensively discusses the design principles, mechanistic foundations, and performance metrics of enzyme–nanomaterial interfaces for energy-related applications. We first outline the fundamentals of enzymatic redox catalysis and the limitations of free enzymes in practical systems. Subsequently, we examine the functional roles of nanomaterials including carbon-based materials, metal and metal oxide nanoparticles, and two-dimensional platforms such as MXenes in facilitating enzyme immobilization and promoting direct or mediated electron transfer. Special emphasis is placed on engineering strategies at the bio–nano interface, including immobilization techniques, surface functionalization, and structural tuning to optimize catalytic efficiency. The review further highlights representative hybrid systems based on laccase, glucose oxidase, peroxidase, and hydrogenase enzymes, and evaluates their applications in biofuel cells, solar–bio hybrid systems, green oxidation reactions, and self-powered biosystems. Stability challenges, deactivation mechanisms, and enhancement strategies such as polymer coatings, cross-linking, and nanoconfinement are critically analyzed. Finally, emerging directions including artificial enzymes, AI-guided catalyst design, and self-healing bioelectrodes are discussed to provide a forward-looking perspective on next-generation sustainable bioelectrocatalytic systems. Full article

Net cleaning robots have been playing an increasingly important role in offshore aquaculture due to their efficiency and labor-saving capabilities. However, in practice, these robots are still entirely teleoperated and require constant, skilled human operation. The mesh intersection points, which serve as a structural feature of the nets, provide valuable visual cues for robot self-localization and net damage identification. Therefore, the detection and tracking of these points are crucial for developing autonomous net cleaning robots. To achieve intersection point detection, we propose NPUNet-lite, a lightweight model based on U-Net. This model significantly minimizes computational resources and model size while preserving high detection accuracy. For reliable point tracking, we develop the NlPTrack algorithm, which incorporates an iterative closest point-based association strategy to meet spatial constraints between points within a frame, and a cascaded association strategy to satisfy homographic and epipolar constraints across adjacent frames. We build a dataset from videos collected during a robotic cleaning task to train and evaluate our methods. The experimental results indicate that our segmentation network achieves comparable accuracy to advanced networks, yet with a substantial reduction in computational cost. Meanwhile, the tracking method successfully tracks the majority of intersection points across scenarios where the robot moves in different directions. Full article

Epoxy resin (E-51) exhibits excellent adhesion and is widely used in the preparation of functional composite coatings. However, its smooth surface lacking micro/nano composite structures limits its self-cleaning capability and optical properties. Direct incorporation of organic silicone or inorganic fillers often faces severe phase separation and filler agglomeration issues, resulting in defects in coating durability and weather resistance. To address these challenges, this study developed a synergistic modification strategy integrating surface energy modulation with the architectural design of micro/nano-structures. Amino-terminated PDMS undergoes ring-opening addition reactions with epoxy groups in the epoxy resin, while functionalized barium sulfate nanoparticles modified with dual silane coupling agents are incorporated to enhance optical properties. This synergistic approach not only resolved interfacial compatibility but also endowed the PDMS@EP-BaSO₄ coating with outstanding comprehensive properties; the water contact angle increased to 123.5°, demonstrating an easy-to-clean benefit. Visible light reflectance reached 95%, and emissivity rose to 90%. Furthermore, when applied to metal surfaces, the coating exhibited excellent stability against acid–alkali–salt corrosion, extreme temperatures, and ultrasonic agitation. This work provided a novel approach for developing protective coatings that integrated high reflectance, high emissivity, and long-term anti-soiling properties. Full article

Surface-enhanced Raman scattering (SERS) has become an effective and sensitive analysis tool for the detection of various molecules. Nevertheless, it is a challenge to fabricate reusable SERS substrates for detecting gaseous molecules. Here, a self-calibrated and reusable SERS substrate has been developed for the quantitative analysis of n-butylamine. The obtained substrate enhances gas enrichment capability through the coordination interaction of Fe₂O₃ with the porous structure of ZIF-8, and strengthens the Raman signal intensity by the localized surface plasmon resonance of Ag nanoparticles. Ethanethiol is employed as an internal standard to enhance analysis accuracy. The substrate exhibits excellent quantitative analysis (linear correlation coefficient, R² = 0.996), signal uniformity (RSD = 6.3%), and batch reproducibility (RSD = 4.8%). Moreover, the substrate achieves self-cleaning through photocatalysis. After five cycles, the substrate retains high SERS activity (RSD = 3.13%), exhibiting excellent reusability. Full article

Artificial intelligence (AI) is increasingly applied to the design and optimization of solar thermal collectors, particularly in the development of selective absorber coatings. This systematic review analyzes recent advances (2020–2026) in AI-driven modeling, optimization, and sustainability strategies for solar thermal technologies following the PRISMA 2020 methodology. The results indicate that current research is largely dominated by Artificial Neural Networks and metaheuristic algorithms, mainly focused on short-term performance prediction and system-level optimization. However, durability, degradation mechanisms, and life-cycle sustainability metrics remain significantly underrepresented in AI-assisted design frameworks. From a materials perspective, recent studies highlight the emergence of multifunctional absorber surfaces, including thermochromic, self-cleaning, and multilayer coatings, often combined with AI-enabled monitoring and digital twin approaches. In addition, sustainable processing routes such as green sol–gel synthesis and low-temperature deposition show strong potential for reducing environmental impact when integrated with AI-based optimization. Nevertheless, the holistic integration of AI with sustainability metrics at the early design stage remains limited. Future research should therefore focus on hybrid and physics-informed AI frameworks capable of simultaneously addressing performance, durability, economic viability, and environmental impact in solar thermal collector design. Full article

Solar absorbers (SAs) are central to building-integrated solar-thermal systems; however, conventional black SAs, despite their high solar absorptance (α_s), offer limited aesthetic flexibility and are therefore poorly suited to modern architectural façades. Brightly colored SAs are widely assumed to suffer from intrinsically low α_s, creating a long-standing trade-off between color vibrancy and energy performance. Here this study reports a dielectric/absorber/dielectric/absorber (D/A/D/A) multilayer architecture, in which the absorber layer is composed of a TiO₂–TiON–C composite, that overcomes this limitation and enables colored solar absorbers (CSAs) with reflectance >20%, α_s > 0.90, wide viewing angles, strong self-cleaning capability, high corrosion resistance and exceptionally long projected service lifetimes. These results demonstrate that vivid coloration and high solar absorptance can be simultaneously achieved without compromising environmental durability. To highlight architectural applicability, we further implement a complementary-color contrast strategy for façade design, yielding visually striking, highly recognizable, and low-cost exterior surfaces. This approach enhances aesthetic integration while significantly strengthening the marketability of CSA-based building envelopes for next-generation sustainable architectural systems. Full article

To address the data quality challenges in ship maintenance data, such as high missing rates, anomalous noise, and multi-source heterogeneity, this paper proposes a data quality enhancement method based on a multi-strategy enhanced Harris Hawks Optimization algorithm for optimizing the Kolmogorov–Arnold Stacked Denoising Autoencoder. First, leveraging the Kolmogorov–Arnold theory, the fixed activation functions of the traditional Stacked Denoising Autoencoder are reconstructed into self-learnable B-spline basis functions. Combined with a grid expansion technique, the KASDAE model is constructed, significantly enhancing its capability to represent complex nonlinear features. Second, the Harris Hawks Optimization algorithm is enhanced by incorporating a Logistic–Tent compound chaotic map, an elite hierarchy strategy, and a nonlinear logarithmic decay mechanism. These improvements effectively balance global exploration and local exploitation, thereby increasing the convergence accuracy and stability for hyperparameter optimization. Building on this, an IHHO-KASDAE collaborative cleaning framework is established to achieve the repair of anomalous data and the imputation of missing values. Experimental results on a real-world ship maintenance dataset demonstrate the effectiveness of the proposed method: it achieves an 18.3% reduction in reconstruction mean squared error under a 20% missing rate compared to the best baseline method; attains an F1-score of 0.89 and an AUC value of 0.929 under a 20% anomaly rate; and stabilizes the final fitness value of the IHHO optimizer at 0.0216, which represents improvements of 31.7%, 25.6%, and 12.2% over the Particle Swarm Optimization, Differential Evolution, and the original HHO algorithm, respectively. The proposed method outperforms traditional statistical methods, deep learning models, and other intelligent optimization algorithms in terms of reconstruction accuracy, anomaly detection robustness, and algorithmic convergence stability, thereby providing a high-quality data foundation for subsequent applications such as maintenance cost prediction and fault diagnosis. Full article

The progressive degradation of surface waters should become one of the most important problems requiring an urgent solution. One of the methods developed is filtering water through loose, degraded sediments, blooms of cyanobacteria or algae, or a bed of hemp (Cannabis sativa L.) waste or hemp fibers. The conducted tests on the percolation of water samples and/or water with sediment from surface waters at sites with different ecological statuses indicate the possibility of using hemp waste for the reclamation of water reservoirs and rivers. The effect of filtration is a rapid improvement in water quality and, consequently, an improvement in the ecological status. The best result was achieved for a small freshwater reservoir with a large number of algae and loose degraded sediment. The initial turbidity value was at the limit of the device’s measurement capability, reaching 9991 NTU. After filtration through the hemp waste bed, the turbidity dropped to 42.52 NTU, a 99.57% decrease. The remaining parameters, C, TDS, and pH, were not subject to significant variability as a result of filtering. Excessive amounts of organic matter, which create a problem for surface waters, are removed. Due to the carrier (hemp waste), which is organic waste, any possible release of small amounts into the aquatic environment will not pose a threat. After applying filtration, a decision can be made on further actions regarding the water reservoir or river: Self-renewal of the reservoir or further percolation using, for example, mill gauze or cleaning the reservoir with other, non-invasive methods. After the filtering procedure, the hemp waste, enriched with organic matter and water remaining in the waste, can be used for composting or directly for soil mulching (preliminary tests have yielded positive results). A hemp waste filter effectively removes Chronomus aprilinus larvae (Chrinomidae) from water. This result indicates the possibility of removing mosquito larvae in malaria-affected areas. The use of hemp filters would reduce the amount of toxic chemicals used to reduce mosquito larvae. Improving the ecological status of surface waters by filtering contaminants with hemp waste filters can reduce the need for chemical treatment. The use of natural, biological filters enables sustainable surface water management. This is crucial in today’s rapidly increasing chemical pollution of surface waters. Full article

In this study, we aimed to develop a high-performance, anti-fouling ultrafiltration membrane by integrating photocatalytic and Fenton-like functions into a polymer matrix, in order to address the critical challenge of membrane fouling and achieve simultaneous separation and degradation of organic pollutants. To this end, a novel Fe-V_O-TiO₂-embedded polyethersulfone (PES) composite membrane was designed and fabricated using a facile phase inversion method. The key innovation lies in the incorporation of Fe-V_O-TiO₂ nanoparticles containing abundant bulk-phase single-electron-trapped oxygen vacancies, which not only modulate membrane morphology and hydrophilicity but also enable sustained generation of reactive oxygen species for the pollutant degradation under light irradiation and H₂O₂. The optimized Fe-V_O-TiO₂-PES-0.04 membrane exhibited a significantly enhanced pure water flux of 222.6 L·m⁻²·h⁻¹ (2.2 times higher than the pure PES membrane) while maintaining a high bovine serum albumin (BSA) retention of 93% and an improved hydrophilic surface. More importantly, the membrane demonstrated efficient and stable synergistic Photocatalytic-Fenton activity, achieving 82% degradation of norfloxacin (NOR) and retaining 75% efficiency after eight consecutive cycles. A key finding is the membrane’s Photocatalytic-Fenton-assisted self-cleaning capability, with an 80% flux recovery after methylene blue (MB) fouling, which was attributed to in situ reactive oxygen species (·OH) generation (verified by ESR). This work provides a feasible strategy for designing multifunctional membranes with enhanced antifouling performance and extended service life through built-in catalytic self-cleaning. Full article

This paper proposes an Online Prototype Angular Balanced Self-Distillation (OPAB) framework to address the challenges posed by non-ideal annotation in remote sensing image semantic segmentation. “Non-ideal annotation” typically refers to scenarios where long-tailed class distributions and label noise coexist in both training and testing sets. Existing methods often tackle these two issues separately, overlooking the conflict between noisy samples and minority classes as well as the unreliable early stopping caused by non-clean validation sets, which exacerbates the model’s tendency to memorize noisy samples. OPAB mitigates the imbalance problem by employing an improved bilateral-branch network (BBN) that integrates max-min angular regularization (MMA) and category-level inverse weighting to achieve balanced hyperspherical representations. The balanced hyperspherical representations further facilitate noise-clean sample separation and early stopping estimation based on large category-wise Local Intrinsic Dimensionality (LID). Moreover, OPAB introduces a bootstrap teacher label refinement strategy coupled with a student full-parameter retraining mechanism to avoid memorizing noisy samples. Experimental results on ISPRS datasets demonstrate that OPAB achieves a 2.0% mIoU improvement under non-ideal annotation conditions and achieves 89% mIoU after cross-set correction, showcasing strong robustness across different backbones and effective iterative calibration capability. Full article

Geothermal energy, recognized as a sustainable and clean resource, is playing an increasingly critical role in the global shift toward low-carbon energy systems. Nevertheless, the exploitation of fractured geothermal reservoirs is often impeded by severe lost circulation during drilling, where conventional plugging materials fail under high-temperature, high-salinity, and high-pressure conditions due to inadequate mechanical strength, poor thermal resistance, and lack of self-adaptive sealing behavior. In response, self-healing materials have emerged as an innovative strategy for developing intelligent lost circulation control technologies. Herein, we report a novel self-healing gel (XFFD) synthesized via inverse emulsion polymerization using acrylamide (AM), acrylic acid (AA), p-nitroblue tetrazolium (PNBT), and modified silica nanoparticles (PAS). The resulting material exhibits exceptional thermal stability, with decomposition onset above 356 °C, as determined by thermogravimetric analysis. Rheological and mechanical assessments reveal outstanding viscoelasticity, moderate swelling capacity (4.17-fold in deionized water), and a high self-recovery efficiency of 91.15%, accompanied by a bearing strength of 3.65 MPa. Mechanistic investigations indicate that the autonomous repair capability stems from dynamic non-covalent interactions—primarily hydrogen bonding and ionic associations—enabled by amide and carboxyl groups within the polymer network. Sand bed filtration tests under simulated geothermal conditions (150 °C, 8% salinity) demonstrate that XFFD forms a robust sealing barrier with significantly shallower invasion depth compared to conventional materials such as sulfonated asphalt and calcium carbonate. This work presents an effective self-healing gel system that ensures reliable wellbore strengthening and fluid loss control in challenging high-temperature, high-salinity geothermal drilling operations. Full article

Throughout the lifetime, road markings (RMs) accumulate dirt, oils, and greases, which reduce visibility, shorten service life, and compromise road safety. If RMs could degrade these pollutants, their service life would increase. When exposed to UV light and humidity, semiconductors, such as titanium dioxide (TiO₂), can interact with contaminants and promote their chemical degradation. Semiconductors are commonly used on different types of substrates to achieve self-cleaning ability. In this study, 0.25–3 wt% TiO₂ was incorporated into a commercial RM paint for this purpose. After functionalization, the RM paint samples were contaminated with Methylene Blue and Rhodamine B. After pollution, the specimens were irradiated with a light source that simulates sunlight. To assess the self-cleaning capacity of the paints, visual analysis, color variation and discoloration by using CIELAB color coordinates, diffuse reflectance, and digital image processing techniques were applied. In both techniques, the samples with 2% and 3% of TiO₂ showed a greater capacity to degrade pollutants. Further, the chemical and morphological characteristics of the reference paint and the samples that showed the best self-cleaning results were analyzed by using Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and X-ray Diffraction (XRD). They identified the polymer, filler, and pigment in the commercial paint and confirmed the TiO₂ increase after functionalization. This study demonstrated the innovative potential of incorporating semiconductors to achieve a new capability (self-cleaning) for RM paints. This breakthrough not only has the potential to extend the RM service life, but also to improve road safety through greater visibility. Full article

A cost-effective laser marker was employed to fabricate a superhydrophilic, photocatalytic Mg-Ti-based surface on glass under ambient conditions. The photocatalytic layer was first deposited via laser processing, followed by partial laser etching to generate micro/nanostructures on the surface. This method preserves partial photocatalytic functionality while enhancing surface roughness and introducing unique nanostructures, enabling the sample to simultaneously exhibit antifogging, self-cleaning capabilities, and high light transmittance. The optimal sample was achieved by tuning laser processing parameters, including repetition rate and scanning hatch distance. It maintained a water contact angle (WCA) of 0° after 15 days of outdoor exposure, which only increased to 21.2° after 30 days. In comparison, the WCA of reference glass increased from an initial 23.3° to 63.9° over the same period. Furthermore, the amount of dust accumulated on the optimal sample was significantly lower—by up to 43%—than that on the reference glass over one month under both indoor and outdoor conditions. After a single spray cleaning, the dust removal efficiency of the indoor-stored optimal sample reached 70%, which was 56% higher than that of the reference. For samples stored outdoors, a single spray removed 67% of the dust from the optimal surface, compared to only 26% for the reference, highlighting its excellent self-cleaning performance. Additionally, the optimal also showcased remarkable antifogging property, which had been maintained over the one-month exposure period without visible degradation. Moreover, the optimal sample exhibited a 2% enhancement in broadband light transmittance across the 400–1000 nm wavelength range, demonstrating strong potential for photovoltaic applications. The simultaneous achievement of antireflection, antifogging, and self-cleaning performance under both indoor and outdoor conditions over a one-month period has rarely been reported in the literature. Full article

This study introduces an innovative microfluidic-based approach for extracting drugs from oral fluids using self-assembled tripeptide hydrogels as sorbents. Peptide microfiber derived from the heterochiral tripeptide ^DLeu-^LPhe-^LPhe was formed in situ within the 14 mm-long microchannel of a two-inlet microfluidic device. The methodology enables the laminar flow-driven mixing of buffer solutions, inducing hydrogel formation at their interface. The resulting fiber exhibited a well-defined morphology and β-sheet structure, confirmed by Raman spectroscopy and Thioflavin T fluorescence. The peptide fibers co-assembled successfully with 5-fluorouracil (5-FU) and naproxen (39.8 ± 1.4 nmol of 5-FU and 27.4 ± 6.6 nmol of naproxen per 112 nmol of peptide used to prepare the fiber), resulting in a molar ratio drug/peptide ratio of approximately 1:3 and 1:4, respectively, demonstrating versatility in drug entrapment. The use of the gel fiber as a sorbent phase was first assessed in buffer, and subsequently, the optimized method was applied to saliva. Adsorption studies under stopped-flow conditions showed a significant drug adsorption capability from buffered solutions by the pre-formed hydrogel (32.8 ± 0.9% of 5-FU and 36.4 ± 3.3% of naproxen per fiber preformed with 112 nmol of peptide), demonstrating their suitability as sorbent material. The extension of the methodology to simulated saliva samples allowed extraction of 36% of 5-FU by the fiber, as determined by ¹⁹F NMR spectroscopy on microcoils, which enabled us to work with the small volume of fluid extracted from the microfluidic device and provided clean spectra and quantitative results. These findings highlight the potential of this tripeptide hydrogel as a sorbent material for therapeutic drug monitoring and toxicological analysis via a simple, non-invasive and rapid approach for drug detection in oral fluids. Full article

Multifunctional coatings integrating high transparency, thermal insulation, and self-cleaning properties are critically needed for optical devices and energy-saving applications, yet simultaneously optimizing these functions remains challenging due to material and structural limitations. This study designed a superhydrophobic transparent thermal insulation coating via synergistic co-construction of micro–nano structures using antimony-doped tin oxide (ATO) and SiO₂ nanoparticles dispersed in an epoxy resin matrix, with surface modification by perfluorodecyltriethoxysilane (PFDTES) and γ-glycidyl ether oxypropyltrimethoxysilane (KH560). The optimal superhydrophobic transparent thermal insulating (SHTTI) coating, prepared with 0.6 g SiO₂ and 0.8 g ATO (SHTTI-0.6-0.8), achieved a water contact angle (WCA) of 162.4°, sliding angle (SA) of 3°, and visible light transmittance of 72% at 520 nm. Under simulated solar irradiation, it reduced interior temperature by 7.3 °C compared to blank glass. The SHTTI-0.6-0.8 coating demonstrated robust mechanical durability by maintaining superhydrophobicity through 40 abrasion cycles, 30 tape-peel tests, and sand impacts, combined with chemical stability, effective self-cleaning capability, and exceptional anti-icing performance that prolonged freezing time to 562 s versus 87 s for blank glass. This work provides a viable strategy for high-performance multifunctional coatings through rational component ratio optimization. Full article