Search Results (84)

Metasurfaces, as emerging functional optical coatings, enable precise wavefront manipulation at the subwavelength scale. In this work, we propose a polarization-encoded switchable structured light generator based on a single-layer holographic metasurface composed of silicon nanopillars. By combining Fresnel holography technology and the Pancharatnam–Berry phase modulation principle, two sets of holographic phase distributions for perfect vortex beams corresponding to orthogonal circularly polarized states are superimposed onto a single metasurface. The optimized nanopillar achieves a transmittance of approximately 85% and a polarization conversion efficiency of around 98% at the wavelength of 632.8 nm. By simply adjusting the incident polarization state, the metasurface generates a perfect vortex beam under right-handed circularly polarized light illumination and a cylindrical vector beam under horizontal linearly polarized light illumination, enabling on-demand switching between the two structured light modes. Furthermore, we design and validate a spatially multiplexed perfect vortex beam generator, in which the radius, topological charge, and focal plane position of each channel can be independently controlled. This flexible thin-film metasurface platform offers a promising route toward compact, multifunctional photonic devices for advanced optical integration. Full article

Artificial intelligence (AI), particularly deep learning (DL), is revolutionizing optical metamaterial design by overcoming the fundamental challenges of multidimensional parameter spaces, nonlinear structure–property relationships, and the intrinsic non-uniqueness of inverse problems. By learning complex mappings between geometric structures and electromagnetic responses, DL enables rapid forward prediction and on-demand inverse design without computationally intensive full-wave simulations. This review provides a comprehensive survey of AI-driven design methodologies across four key metamaterial platforms: localized resonant nanostructures, metasurfaces, periodic and guided-wave photonic structures, and complex scattering systems. For each platform, we systematically examine the neural network architectures employed, the specific design challenges addressed, and the representative achievements attained. These data-driven approaches not only significantly accelerate the discovery of high-performance structures but also offer new opportunities for extracting physical insights into light–matter interactions. We assess the critical challenges of data efficiency, model interpretability, and experimental feasibility, and outline emerging research directions that may address these barriers. This review aims to provide both a comprehensive summary of the current state of the art and forward-looking perspectives for this rapidly evolving interdisciplinary field. Full article

Chemotherapy has been used in cancer treatment for decades; however, standard chemotherapy treatments still have significant weaknesses, including collateral damage to healthy tissue, rapid development of drug resistance, and dose-limiting toxicity that limits therapeutic value. There is now an alternative approach using polymer materials that are responsive to biological stimuli that will allow for improved treatment of cancer while avoiding the limitations. Responsive polymer materials are designed to be inert during circulation until they reach their site of action; then, they will respond to specific triggers. These smart carriers respond to stimuli present in the tumor microenvironment (e.g., low pH, high glutathione levels, and increased proteolytic activity) or external stimuli applied at the bedside (e.g., localized heat, light, ultrasound, and applied magnetic fields). In both cases, there is a consistent pattern where the drug is released exactly where/when it is needed, with minimal drug release occurring outside that location and timeframe. Therefore, it is theorized that the use of polymeric-based delivery systems with stimuli-regulated drug release will significantly increase the concentration of drug delivered intratumorally, decrease the drug toxicity, and provide a potential mechanism to overcome the development of multidrug resistance from a variety of cancer treatments. To date, various types of responsive polymers have been developed and could be combined to give rise to a wide variety of different vehicle systems (e.g., micelles, nanogels, hydrogels, and hybrid delivery systems), with many of these carriers designed to respond to multiple stimuli simultaneously. Nonetheless, significant challenges remain in the clinical application of these materials due to tumor heterogeneity, immune system interactions, reproducibility issues, polymer chemistry advances, surface chemistry, and other interaction mechanisms. As a result of all of these evolving regulatory systems, as well as some of the emerging areas of polymer chemistry and surface engineering, theranostic integration will allow for new routes to provide therapy for patients with cancer. Additionally, because of these scientific advances, there will also be more opportunities to provide targeted, controllable, and on-demand treatments to patients using stimuli-responsive polymers. Full article

Light-triggered hydrogel systems offer precise spatiotemporal control over drug release, yet most existing approaches require direct chemical conjugation of a photocleavable linker to the payload, which risks compromising bioactivity and limits applicability to structurally diverse molecules. Here, we report a gelatin–poly(ethylene glycol) (PEG) hybrid hydrogel crosslinked via strain-promoted azide–alkyne cycloaddition (SPAAC) click chemistry, in which an o-nitrobenzyl photocleavable (PC) linker is incorporated into the PEG crosslinker arm rather than conjugated to the drug. Acetylated gelatin–azide (AGA) was synthesized by sequential azide functionalization and amine capping of gelatin, and four-arm PEG-PC-DBCO (4armPEG-PC-DBCO) was prepared by coupling a PC DBCO-PEG4-NHS ester to four-arm PEG amine. Successful incorporation of the azide, DBCO, and o-nitrobenzyl moieties was confirmed by FT-IR spectroscopy, ¹H NMR spectroscopy, and UV-Vis spectrophotometry. Hydrogel formation under physiological conditions (PBS, 37 °C) without catalysts or initiators was verified by rheological frequency sweep analysis, which confirmed elastic-dominant behavior (G′ > G″). Upon irradiation at 365 nm, the crosslinker was cleaved, and rapid network dissolution was observed both macroscopically and by in situ time sweep rheology. This platform enables on-demand, UV-selective hydrogel degradation independently of payload identity, providing a versatile foundation for future controlled drug release applications and dynamic, on-demand degradable scaffolds for tissue engineering. Full article

Synthetic biology seeks to build predictable, programmable biological systems. We developed a blue-light-inducible T7RNAP system with dual-input regulation to enable precise spatiotemporal gene control, which is vital for biomanufacturing, therapy, and microbial engineering. We optimized it by replacing RBS sequences, testing tandem T7 promoters, and evaluating split-T7RNAP variants. Expression and bactericidal efficacy were assessed via fluorescent output and real-time growth curves under blue light. RBS variants caused up to 50-fold differences in expression. Three tandem T7 promoters provided the best balance between yield and fidelity. Integration of a benzoate-responsive module enabled 4.5-fold repression at 3 mM benzoate, demonstrating effective chemical off-switching without compromising light induction. This system combines blue light precision with environmental responsiveness, offering non-invasive, on-demand activation for antimicrobial therapy or spatial bioproduction. The benzoate-triggered off-switch is especially valuable for ecological applications such as biocontainment or bioremediation, where gene expression must shut down upon detection of pollutants, for example, aromatic hydrocarbons. Its orthogonal, modular design supports context-dependent control, making it ideal for environmental biosensors, programmable probiotics, and smart antimicrobial delivery in complex ecosystems. Full article

The integration of micro-nano optical structures has become an essential strategy for overcoming the performance bottlenecks of quantum well infrared photodetectors (QWIPs), specifically by addressing the inherent inability of planar devices to couple with normally incident light due to intersubband transition selection rules. A critical factor in this integration is the precise spectral overlap between an optical mode and the material’s excitation mode. Therefore, achieving precise spectral engineering is indispensable. However, conventional electromagnetic simulations act as forward solvers, calculating optical responses based on given geometric parameters. They cannot directly perform inverse design, which involves deriving optimal geometric parameters directly from a desired optical response. Consequently, structural optimization is severely constrained by time-consuming trial-and-error iterations, which often struggle to find the global optimum in a complex design space. To overcome these limitations, this paper presents a comprehensive theoretical and numerical study proposing a deep learning framework for QWIPs coupled with all-dielectric micropillar structures. By establishing a structure-absorption spectrum dataset via finite difference time domain (FDTD) simulations, we developed a dual-network setup. For the forward prediction, a multilayer perceptron (MLP) maps geometric parameters (side length a and period p) to the absorption spectrum, achieving a computational speedup of seven orders of magnitude over traditional numerical simulations. Concurrently, a convolutional neural network (CNN) is employed for the inverse design, realizing on-demand design of geometric parameters based on target spectra with high reconstruction accuracy. Furthermore, the selected all-dielectric micropillar structures are highly compatible with mainstream semiconductor fabrication processes. This research provides an efficient, automated toolkit for the development of high-performance infrared photodetectors. Full article

Food delivery logistics sustainability includes environmental impact, economic efficiency, and service quality. Traditional logistics models mainly rely on fixed “pickup buffer” policies (such as a set 10 min wait). These systems do not account for the changing nature of restaurant operations and delivery conditions, leading to higher operating costs, driver idle time, and poorer food quality. To move delivery systems from reactive decision-making to proactive, dynamically forecasted operations, an adaptive control mechanism is needed. In on-demand food delivery, this offers a clear path to sustainability through better dispatch accuracy, order prep, and pickup coordination. To resolve these bottlenecks, this study examines how a smart logistics framework based on a dynamic Gradient Boosting Regressor (GBR) and policy-sensitive GBR can provide more accurate estimates of drivers’ waiting times in light of contextual factors such as rush hour, time of day, and operational constraints. In last-mile food delivery, the proposed method aims to reduce operational costs, improve scheduling effectiveness, and maximize resource utilization by moving beyond static, predefined waiting periods to adaptive, context-aware decisions. The developed framework analyzes a proprietary dataset of 368,250 instant orders from a major Saudi Arabian logistics provider to evaluate the efficacy of static thresholds versus a proposed predictive, dynamic machine-learning-based approach. After rigorous data cleaning and temporal-logic adjustments, a “High-Fidelity Ground-Truth” subset of 1842 verified orders is used to simulate policy performance. This 99.5% reduction is necessitated by the widespread absence of the “Order Ready” timestamp in operational logs, which is the critical target variable for supervised learning; comparative analysis confirms the subset remains representative of the parent population’s spatiotemporal dynamics. The baseline analysis reveals severe inefficiencies in the static model, with a 61.67% violation rate for driver wait times, particularly in Riyadh ($p<0.001$) and during late-night operations. The simulation results demonstrate that the dynamic policy reduces the “Buffer Miss Rate” (premature driver arrivals) from 59.08% to 7.32%, resulting in a 68.5% reduction in total operational waste costs. Full article

Metasurfaces offer an unprecedented avenue to facilitate light-matter interactions. However, traditional design methodologies rely on computationally intensive trial-and-error processes. Moreover, existing deep learning (DL) schemes are predominantly hindered by their massive data requirements and limited exploration of freeform design spaces. To overcome these challenges, a multi-model-driven generative-evolutionary strategy (GES) is proposed, for the on-demand inverse design of bespoke Terahertz (THz) metasurface sensors. By leveraging a Conditional Diffusion Generator (CDG) and an Attention-Enhanced Residual Network (ARN), this framework enables the exploration of an expansive design space encompassing $2^{100}$ possible configurations. The GES effectively overcomes the data bottleneck by selectively generating high-potential data in stages. Full-wave simulations confirm that the inversely designed metasurfaces exhibit high-contrast resonance peaks and exceptional sensitivity across low, mid, and high THz bands. This work provides a versatile paradigm for the efficient design of high-performance functional metamaterials, significantly accelerating the advancement of application-specific THz sensing. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disease that imposes substantial physical, emotional, and socioeconomic burdens on patients. Conventional therapeutic approaches are often limited by systemic toxicity, inadequate joint targeting, and variable patient responses, highlighting the urgent need for advanced drug delivery systems. Smart hydrogels have emerged as a promising platform for RA treatment due to their unique three-dimensional hydrophilic networks, excellent biocompatibility, and tunable physicochemical properties. This review systematically summarizes the preparation strategies and design principles of smart hydrogels, with an emphasis on chemically and physically crosslinked networks as well as composite systems. It further outlines the major stimulus-responsive release mechanisms—including temperature, pH, reactive oxygen species (ROS), light, and enzyme triggers—that enable targeted and controlled drug delivery within the inflamed joint microenvironment. Among the various types discussed, temperature-responsive and multi-responsive hydrogels are most frequently investigated for their potential to achieve localized, on-demand therapy. Despite considerable preclinical progress, the clinical translation of smart hydrogels faces critical challenges, including insufficient long-term biocompatibility data, lack of standardized evaluation protocols, and difficulties in scalable manufacturing. This review aims to provide a conceptual framework for the rational design of smart hydrogels and to stimulate interdisciplinary efforts toward overcoming existing translational barriers in RA treatment. Full article

Local analgesia is a prevalent and cost-effective pain management strategy with minimal systemic side effects. At the same time, nanotechnology has been employed to achieve the sustained release of drugs to prolong the duration of local anesthetics (LAs). However, these traditional nanoformulations lack responsiveness and thus cannot achieve precise pain relief through on-demand administration. The emergence of “intelligent” nanosystems with stimulus-response capabilities has opened up new prospects in pain control. This review summarizes recent advancements in the design and application of triggerable LAs nanoformulations that can be activated by external stimuli such as light, ultrasound, or heat. These systems facilitate precise, patient-specific pain management, transforming the clinical approach from long-term suppression to controllable and on-demand relief. Finally, we discussed the current challenges and future prospects. A deeper understanding of this field will facilitate the development of superior analgesic solutions and ultimately improve the treatment outcomes for patients. Full article

Solar-driven hydrogen production is a cornerstone of sustainable energy systems, yet its implementation remains intrinsically constrained by reliance on continuous illumination, limiting temporal control and compatibility with intermittent renewable sources. This perspective articulates the emerging concept of on-demand solar hydrogen generation, in which photon absorption is intentionally decoupled from hydrogen evolution through reversible charge storage and stimuli-responsive catalytic activation. We introduce a systematic classification of on-demand approaches across molecular, semiconductor, and device-level platforms, highlighting how these architectures enable programmable hydrogen release triggered by electrical, chemical, or thermal stimuli and sustained operation beyond illumination periods. Moving beyond a descriptive survey, we propose key performance metrics, including Switching Efficiency, Response Time, and Cycle Fidelity, to enable consistent evaluation and comparison of on-demand systems. Recent advances demonstrate substantial progress in charge storage, catalytic reversibility, and dynamic control, directly addressing the intermittency limitations of conventional photocatalytic and photoelectrochemical technologies. While challenges remain in kinetic synchronization, durability, and scalability, on-demand hydrogen concepts establish a coherent design framework for flexible and dispatchable solar fuels. By enabling integration with variable renewable inputs, this paradigm points toward adaptive and intelligent solar-fuel systems applicable from grid stabilization to off-grid and extraterrestrial environments. Full article

This study evaluates the On-Demand All-Red Interval (ODAR) at signalized intersections to address red-light running (RLR) issues. Traditional fixed all-red intervals fail to adapt to dynamic traffic conditions, leading to potential safety risks and unnecessary delays. This study introduces a novel approach for dynamically extending the all-red interval on demand to enhance intersection efficiency while maintaining safety by eliminating unnecessary clearance intervals when no risk exists. Utilizing software-in-the-loop simulation, the study assesses the effectiveness of the ODAR method compared to conventional fixed-duration and Dynamic All-Red Extension (DARE) methods, allowing realistic controller testing without field deployment. The ODAR method adapts to real-time traffic conditions by incorporating vehicle speed and signal timing, ensuring vehicles with high collision risk clear the intersection safely. The study is conducted using a microsimulation model based on the Washington Street arterial network in Lake County, Illinois, validated against real traffic conditions. The results demonstrate that ODAR increases throughput and, in specific scenarios, reduces delays and stop occurrences compared to FAR and DARE strategies, based on a field-calibrated microsimulation dataset of a real-world arterial corridor. Importantly, these efficiency improvements are achieved while maintaining comparable intersection safety outcomes, as measured by red-light-running events, conflict frequency, and conflict severity. Full article

This systematic review assesses indoor air quality (IAQ) in tropical residences (Köppen Af/Am/Aw), explicitly linking IAQ to ventilation from in situ monitoring and, when relevant, occupant surveys (surveys synthesized qualitatively). This focus is warranted by the scarcity of tropical, housing-specific evidence. Searches were performed exclusively in Google Scholar (25 August 2024–5 August 2025; English/Spanish) under PRISMA, with documented queries/filters; eligible studies reported residential settings, tropical climate, and IAQ–ventilation linkage. Results show a regulatory mosaic with few binding residential limits and heterogeneous protocols that hinder comparison. Robust patterns include cooking-related particle peaks, penetration of traffic dust, humidity-driven VOC/formaldehyde emissions, and mold growth under deficient hygrothermal control. CO₂ is a useful operational indicator of ventilation yet insufficient for risk assessment without PM and VOC monitoring. Evidence supports source control, cross-ventilation and/or on-demand extraction/outdoor-air supply, humidity management, and filtration/purification to avoid particle ingress during ventilation. Reporting of sensor performance (calibration, drift, RH/T effects) is inconsistent, and targeted evaluations of TVOC/formaldehyde and window screens (mesh) are scarce. We conclude that tropical residential IAQ management requires multi-parameter, continuous monitoring, standardized reporting, and trials integrating ventilation, dehumidification, and filtration under real occupancy, alongside adaptive regulation and passive tropical design augmented by light mechanical support and informed occupant behavior. Full article

Background: Digital light processing (DLP) 3D printing has emerged as a rapid alternative to labour-intensive micro-moulding for producing microneedle (MN) arrays, yet its use in biodegradable, dissolving MNs has been limited by proprietary, non-degradable resins. Methods: The current study proposed an innovative, biocompatible PEGDA–vinyl-pyrrolidone photo-resin with lithium phenyl(2,4,6-trimethylbenzoyl) phosphinate initiator, which systematically optimises its rheology and photo-reactivity for DLP printing. Resin formulations were evaluated through viscosity profiling, cure kinetics, FTIR, and ¹H NMR, and MN arrays were printed using a desktop DLP platform and characterised by optical microscopy, mechanical testing, thermal analysis, and dissolution studies. Results: A 40% PEGDA up-to 100% VP blend with 0.4% initiator was identified as providing rapid photopolymerisation, low shrinkage and complete vinyl conversion. Using a desktop DLP platform, 6 × 6 MN patches were printed in a single step without moulds and analysed by optical and scanning electron microscopy. The printed MNs reproduced CAD dimensions with <3% deviation, achieving a height of 1.40 ± 0.02 mm and a base thickness of 1.00 ± 0.01 mm, and showed a tip radius consistent with sharp penetration. Compression testing measured an array force of 32 N, corresponding to ~0.9 N per needle, exceeding the 0.2 N threshold for skin insertion. FTIR and ¹H NMR confirmed near-quantitative crosslinking, thermogravimetric and differential scanning calorimetry indicated stability at ambient conditions, and dissolution studies showed complete needle dissolution. Conclusions: An optimised PEGDA/VP resin yields geometrically precise, mechanically robust dissolving MNs in a single step, addressing the limitations of micro-moulding and paving the way for customisable, on-demand transdermal delivery of active molecules and biologics. Full article

In protected horticulture, precise regulation of light intensity [i.e., photosynthetic photon flux density (PPFD)], ambient temperature, and ambient CO₂ concentration is crucial for optimizing crop photosynthesis. Tomatoes, a key greenhouse crop, exhibit temporal variations in photosynthetic efficiency across their growth cycle. However, the differences in the dynamic responses of net photosynthetic rate (Pn) of tomatoes to environmental factors during flowering and fruit development stages in winter solar greenhouses, as well as how to utilize these differences respectively to achieve more precise on-demand environmental regulation, still require in-depth exploration. Based on measured data, this study employed decision tree (DT), random forest (RF), and XGBoost (XGB) models to predict net photosynthetic rate (Pn) across two growth periods. The results demonstrated that, in comparison with the early flowering stage, the photosynthetic potential of tomato leaves increased during the fruit development stage, with the Pn peak increasing by 11.5%. The proportion of observed data points in the high Pn range (25–35 μmol m⁻² s⁻¹) at the fruit development stage was 14.2%, which was significantly higher than the 6.7% observed at the early flowering stage. Meanwhile, the sensitivity of tomato leaves to changes in environmental factors also increased during the fruit development stage. On the independent test set, the XGB model exhibited the best predictive performance: the root mean square error (RMSE) for the early flowering stage model was 0.47 μmol m⁻² s⁻¹, with a mean absolute error (MAE) of 0.36 μmol m⁻² s⁻¹; for the fruit development stage, the RMSE was 0.60 μmol m⁻² s⁻¹, and the MAE was 0.41 μmol m⁻² s⁻¹. This study demonstrated the variation patterns of photosynthetic characteristics of tomatoes at different growth stages in response to environment factors. The established XGB model and the generated three-dimensional visualized Pn prediction surfaces provide a quantitative basis and decision-support tools to facilitate precise environmental management strategies for the coordinated dynamic regulation of light, temperature, and CO₂ in solar greenhouses. Full article