Type 2 diabetes mellitus (T2DM) is a global health challenge characterized by chronic hyperglycemia and oxidative stress. Pithecellobium dulce root has long been recognized for its antidiabetic potential; however, its specific bioactive constituents and mechanisms of action remain poorly defined. This study aimed to evaluate the antidiabetic and antioxidant properties of extracts and isolated molecules from P. dulce root bark. The DCM/MeOH crude extract of P. dulce root bark was fractionated with n-hexane (PDEH) and ethyl acetate (PDAE), followed by chromatographic purification and spectroscopic characterization, yielding seventeen compounds (1-17). The antioxidant activity (DPPH, ABTS, FRAP) and antidiabetic potential of PDEH, PDAE, and 1-17 were assessed in vitro using yeast-derived enzymes and in silico (targeting human α-glucosidase [PDB: 2QLY] and human α-amylase [PDB: 4GQR]). The in vitro α-glucosidase experiments used saccharomyces cerevisiae enzyme, which varies from the human target. Therefore, these results should be taken as preliminary screening data that needs confirmation with human enzymes. Compound 1 was identified as new, while 2 was isolated for the first time from a natural source. The cell-free chemical tests DPPH, ABTS, and FRAP measured antioxidant capability. These tests quantify radical-scavenging and electron-transfer capabilities in vitro and are preliminary chemical screening methods. They do not directly represent biological antioxidant activity in cells or organisms. PDEH demonstrated strong radical scavenging against DPPH (IC50 = 15.30 μg/mL) and ABTS (IC50 = 12.80 μg/mL), while pristriol (16) showed ferric reducing power (EC50 = 4200 μM FeSO4/g). Enzyme inhibition assays demonstrated activity against α-amylase (IC50 53.88-112.24 µg/mL; acarbose IC50 = 91.20 µg/mL) and α-glucosidase (IC50 18.38-136.88 µg/mL; acarbose IC50 = 11.31 µg/mL). Compounds 15, 1, and 2 showed superior activity compared to acarbose for α-amylase, with effect sizes (Cohen's d) of 2.15, 0.94, and 0.82, respectively, and IC50 values of 53.88, 88.15, and 92.62 µg/mL; for α-glucosidase, IC50 values were 18.38, 39.25, and 36.40 µg/mL, respectively. Docking studies supported these findings, revealing binding energies of -9.08, -8.34, and -7.22 kcal/mol for compounds 1, 2, and 15 with α-amylase, and -10.35 and -9.79 kcal/mol for compounds 1 and 2 with α-glucosidase. ADME profiling further identified 1 and 2 as promising lead candidates for dual-enzyme inhibition. P. dulce root bark represents a potent source of bioactive molecules with both antioxidant and dual-enzyme-inhibitory properties. These findings validate its traditional use and highlight its potential in the development of multitarget therapies for T2DM management.
This paper proposes a three-tier Stackelberg game-based hierarchical optimization framework for integrated electric vehicle (EV) battery swapping stations (BSS) and charging point operator (CPO) systems. The framework models the strategic interactions among three decision-making layers comprising grid operators, integrated CPO-BSS operators, and EV users within a multi-stakeholder energy management environment. A bi-level mixed-integer linear programming (MILP) formulation combined with backward-induction-based Subgame Perfect Nash Equilibrium (SPNE) analysis is developed to optimize dynamic electricity pricing, battery charging and swapping schedules, grid power utilization, and user service decisions under operational and grid constraints. The upper layer determines time-varying tariffs, demand-response incentives, and capacity charges to improve grid stability and social welfare, while the middle layer optimizes integrated charging-swapping operations and battery inventory management in response to grid signals and user behavior. The lower layer models EV users as rational followers responding to dynamic pricing through charging or swapping decisions. The proposed framework is validated using EV charging sessions from the publicly available ACN-Data corpus from which BSS swapping demand inputs were synthetically derived via a principled data-mapping procedure and Italian GME day-ahead electricity market price data. The results show that the proposed hierarchical framework reduces the operational cost of the system by 14.2-26.5% when compared with the unoptimized baseline system over the five-year simulation period (2020-2024), while reducing the peak grid demand by 26-28% (192-204 kW) compared with the unoptimized system and maintaining 96.8% service reliability. The coordinated strategy further enables effective load shifting toward low-price periods, enhances battery utilization efficiency, and improves demand elasticity through dynamic pricing mechanisms. Comparative analysis shows that the proposed framework captures 15-22% additional value over decentralized Nash equilibrium strategies while achieving near-optimal centralized social welfare performance under realistic institutional and operational constraints. Sensitivity and benchmarking studies confirm the robustness, computational tractability, and scalability of the proposed approach across varying tariff structures, battery inventories, and demand scenarios. The framework provides practical insights for EV infrastructure planning, grid-aware energy management, and regulatory policy design for future integrated charging and battery swapping ecosystems.
With the rapid construction of new power systems characterized by high renewable energy penetration, high power electronics integration, and high voltage levels, the insulation reliability of critical power equipment-including cable accessories, gas-insulated switchgear (GIS), and power electronic modules-faces unprecedented challenges. Field grading materials (FGM), as core functional media for adaptive electric field homogenization and insulation failure prevention, have emerged as a research hotspot spanning materials science, electrical engineering, and polymer engineering. Starting from the current research status of FGM, this review systematically summarizes filler optimization strategies, covering single fillers, hybrid fillers, trace co-fillers, and structural modification approaches. The applications of FGM in transmission cables, GIS, high-voltage electrical machines, and wide-bandgap power electronic modules are then elaborated in detail. Emphasis is placed on performance enhancement routes of FGM, particularly thermal conductivity improvement via constructing three-dimensional thermally conductive networks and intelligent early warning based on thermochromic materials. Finally, the existing bottlenecks of FGM are analyzed in terms of material stability, multi-physical field coupling adaptation, and engineering industrialization. Future development trends are prospected toward high-performance, multifunctional, intelligent, and engineering-oriented FGM. This review aims to provide theoretical references and technical support for the design and application of advanced FGM in new power systems.
We developed N-doped carbon quantum dots (N-CQDs) as both photocathode modifiers and electrolytes for self-powered portable photoelectrochemical (PEC) cells to generate electricity under visible-light illumination. Using betaine-type Meldonium precursor, and either ethylenediamine, N,N-dimethylformamide or NH3·H2O as a nitrogen source deliver three different N-CQDs featuring both surface-negatively-charged and positively-charged groups. Due to structural-directing template functionality of ethylenediamine, N-CQDs(en) incorporates the highest pyridinic-N content, which facilitates the charge conductivity and fine-regulates the reaction selectivity within Csp2-frameworks. As semiconductor-coatings electrodeposited on Cu2O, N-CQDs(en) integrate with Cu2O into heterojunctions (N-CQDs(en)/Cu2O) to improve charge separation and promote 4e- oxygen-reduction into water. Importantly, encapsulating an aqueous solution containing N-CQDs(en) in a gelatin/sodium L-pyroglutamate-derived conductor gives a quasi-solid-state electrolyte that facilitates the charge migration, improving the electrodes-electrolytes interfacial incompatibility, while also possibly helping to in situ complement active sites on the modified photocathode. Coupled with a FeNiOOH/FeN-decorated BiVO4 photoanode, enabling the efficient 4e- water oxidation, the complete system establishes a self-sustaining H2O-O2-H2O cycle. The resulting PEC cell shows impressive electricity output for over 120 h under irradiation, enough to power some small electronics. Unlike conventional photovoltaics, this cell is moisture-tolerant, oxidation-resistant and concurrently harnesses light and chemical energy, presenting a new paradigm for next-generation light-to-electricity conversion.
Perovskite solar cells have achieved rapid efficiency gains; however, interfacial recombination and energy-level mismatch remain major factors limiting performance consistency and stability. This work presents a systematic numerical investigation of interface engineering in perovskite solar cells using SCAPS-1D simulations. Three absorber materials of methylammonium lead iodide (MAPbI3), formamidinium lead iodide (FAPbI3), and cesium lead iodide (CsPbI3) are examined within an identical device configuration employing TiO2 as the electron transport layer and spiro-OMeTAD as the hole transport layer. A thin molybdenum disulfide (MoS2) layer is introduced between the absorber and the hole transport layer to evaluate its influence on the band alignment, electric potential distribution, and photovoltaic performance. The results show that MoS2 modifies the valence band alignment at the back interface, increasing the local electric potential and supporting an improved charge separation. Devices incorporating MoS2 exhibit enhanced short-circuit current density, fill factor, and power conversion efficiency across all absorber compositions. Efficiency improvements from 19.28% to 21.54% for MAPbI3, from 20.10% to 22.25% for FAPbI3, and from 15.32% to 18.25% for CsPbI3 were observed. Parametric analyses further indicate an improved tolerance to variations in absorber thickness, doping concentration, temperature, and bulk defect density in MoS2-integrated structures. These findings demonstrate that MoS2-based interface modification offers a consistent pathway to reduce interfacial losses and improve the photovoltaic performance across hybrid and all-inorganic perovskite solar cells.
Polymer-electrolyte membrane fuel cells (PEMFCs) deliver high efficiencies and leading power densities, making them a promising cornerstone technology for decarbonization across transportation, stationary power, and emerging distributed energy systems. The ion exchange membrane (IEM) dictates proton transport, governs water and gas management, and eventually constrains durability and feasibility. Although perfluorosulfonic acid membranes have long defined the state-of-the-art, their environmental footprint, supply-chain limitations, and intrinsic performance trade-offs have intensified efforts to develop sustainable, high-performance alternatives. Among the most compelling candidates to emerge are pyridinyl-based IEMs, a rapidly expanding materials family distinguished by exceptional oxidative and hydrolytic stability, versatile molecular design, and competitive ionic conductivity. This review critically evaluates progress across the diverse landscape of pyridinyl-based IEMs, including pyridinyl- and pyridinium-functionalized hydrocarbon backbones, and hybrid architectures and discusses how molecular design principles translate into macroscopic transport properties and cell performance. We further outline the growing relevance of these materials beyond PEMFCs, highlighting their integration into electrochemical and separation platforms. Finally, we identify the key scientific and engineering challenges that remain, ranging from achieving long-term chemical durability under realistic operating conditions to scaling synthetic routes and integrating membranes into commercial architectures, and propose research directions to accelerate the maturation of pyridinyl-based IEMs.
With the rapid expansion of electric mobility and large-scale energy storage systems, the high-value regeneration of graphite anodes from retired lithium-ion batteries has attracted increasing attention. The degradation mechanism of graphite anodes involves multiple factors, including bulk structural damage and interfacial deterioration during cycling. However, state-of-the-art regeneration approaches have been restricted to addressing either structural degradation or interfacial instability in isolation, precluding the attainment of desirable electrochemical performance. To circumvent this fundamental limitation, we propose a phytic acid-assisted regeneration strategy for spent graphite anodes. This approach leverages the abundant intrinsic defects and edge sites present in cycled graphite, which serve as preferential reactive sites for subsequent modification. Upon subsequent facile thermal treatment, phosphorus species are incorporated into the graphite matrix, enabling bulk doping-induced structural reconstruction while simultaneously optimizing the surface chemistry and interfacial properties. Comprehensive characterizations reveal that a fraction of the incorporated phosphorus species diffuses into the bulk lattice and promotes structural restoration. These dopants modulate the local electronic structure and bonding configuration, thereby facilitating lithium-ion adsorption and accelerating diffusion kinetics. Meanwhile, the residual phosphorus species at the surface direct interfacial reactions toward the formation of a robust, inorganic-dominated solid electrolyte interphase (SEI) layer enriched with LixPOy species, which significantly boosts Coulombic efficiency and long-term cycling stability. As a consequence, the revitalized graphite exhibits excellent electrochemical performance, delivering a specific capacity of 378 mAh g-1 after 500 cycles at a current density of 1 C. Moreover, it exhibits good rate capability, maintaining 330 mAh g-1 at 2.0 C. Further kinetic and interfacial analyses reveal that the improved performance is supported by enhanced charge transfer and ion diffusion, along with a stable and uniform interfacial structure. This work provides a simple and promising route for the high-value utilization of spent graphite and the sustainable design of energy storage materials.
Chronic wounds constitute a major global clinical challenge, whereas traditional dressings lack real-time monitoring and active therapeutic capabilities. Self-powered thermoelectric gel dressings that integrate thermoelectric conversion and flexible gel networks have emerged as a transformative platform for wound care, enabling the seamless integration of sensing and therapy. This review systematically summarizes the fundamental energy conversion mechanisms and multi-scale material design strategies of thermoelectric gels, along with the essential fabrication processes, electrode engineering, and integration technologies for practical applications. It further highlights two core functions of these dressings: self-powered multimodal monitoring of wound temperature, pressure, and biomarkers, and in situ pro-healing effects through electrical stimulation that modulates inflammation, cellular behavior, and angiogenesis. Recent advances in the personalized management of diabetic foot ulcers, infected wounds, and athletic injuries using these dressings are also summarized. Finally, the key existing challenges and future development trends are critically analyzed, providing a comprehensive theoretical and technical framework for the advancement of next-generation intelligent wound dressings.
Parkinson's disease (PD) is a progressive neurodegenerative disorder where tremor remains one of the most prominent and disabling motor symptoms. Traditional clinical rating scales for disease severity rely on clinician observation and patient self-report, often failing to capture the dynamic and continuous nature of tremors in daily life. This drives the development of objective monitoring technologies, such as wearable sensors, for more accurate evaluation of PD severity. However, many existing systems use rigid materials that lack the mechanical compliance and skin conformability required for stable biointegration. This review summarizes advances in flexible wearable sensors for PD tremor assessment from material innovations to a device engineering perspective, covering inertial measurement units (IMUs), electromyography (EMG), and emerging self-powered systems such as triboelectric (TENG) and piezoelectric nanogenerators (PENG). This review highlightshow functional materials, microstructural design, and device architectures govern sensing mechanisms and performance, with particular emphasis on the transition from rigid components to soft, skin-interfaced technologies. Recent patent activity reflects a shift toward multimodal, wireless, and clinically integrated platforms. Despite progress, challenges remain, including motion artifacts, durability, and limited large-scale clinical validation. Integration of flexible materials, self-powered designs, and AI-driven analytics enables continuous, personalized monitoring, moving closer to real-world clinical deployment and improved patient care.
The pursuit of carbon neutrality in China demands a rapid, spatially informed scale-up of renewable energy, including biomass, yet high-resolution, policy-aware data for site-specific planning remain scarce. To bridge this gap, we develop China's high-resolution spatially explicit biomass resource potential dataset, which integrates five biomass categories (agricultural residues, forestry residues, energy crops, animal manure, and municipal waste) at 1 km resolution for 2020, with projections to 2050. This dataset incorporates key constraints such as food security, ecological conservation, and land use suitability. It provides heat value potential distribution maps in GeoTIFF and PDF formats, and heat value potential data in Excel format. By combining multi-source geospatial data, statistical downscaling, and machine learning, this dataset enables precise assessment of resource conditions and provides forward-looking planning for biomass power deployment, rural revitalization, and carbon reduction strategies, thereby meeting China's critical need for integrated, location-aware open data in energy and land-use decision-making.
The ionic thermoelectric (i-TE) technology offers a compelling pathway for harvesting low-grade heat, distinguished by its exceptionally high thermopower and inherent material versatility. However, development in this field is constrained by the complex interplay among electrochemical, thermodynamic, and transport phenomena, which poses significant challenges to the fundamental understanding, accurate performance evaluation, and systematic screening of new materials. This review provides a systematic overview and outlook of the i-TE landscape, bridging fundamental principles with future applications. We begin by deconstructing the core components-electrolytes and electrodes-to elucidate the material design strategies that govern the performance. The discussion then progresses to a multi-scale evaluation of key metrics, from intrinsic i-TE properties to device-level energy conversion and storage capabilities. A central focus is placed on dissecting the persistent chemical and physical challenges, including ion selectivity, transport dynamics, and interfacial engineering. This review further surveys the emerging applications of i-TE, ranging from wearable power generation and active cooling to multimodal sensing and integrated multifunctional systems. Furthermore, we highlight the paradigm-shifting potential of synergistic systems, where coupling thermoelectric effects with electrochemical, photocatalytic, or hydrovoltaic processes unlocks unprecedented functionalities and performance enhancements. Ultimately, this review synthesizes current understanding to propose a strategic roadmap for this field. It outlines the key scientific and engineering perspectives on standardization, scalable manufacturing, and reliability that are essential to translate laboratory innovations into viable commercial technologies.
Sesame oil is a valuable edible oil whose authenticity and quality are important concerns for consumers and producers. Reliable and rapid analytical methods are therefore required to assess the quality and authenticity of sesame oil products available on the market. The electronic nose (E-nose) is a non-destructive, portable, and non-contact tool that offers advantages such as low cost, high speed, and ease of use compared with conventional quality control methods. In this study, E-nose and GC-MS techniques were applied to evaluate pure sesame oil and four commercially available sesame oil samples. Based on E-nose data analyzed using chemometric methods, the PCA model explained 96% of the total variance, while LDA and QDA achieved 100% classification accuracy. SVM and ANN models showed accuracies of 98.67% and 98.7%, respectively. The results demonstrated a strong agreement between E-nose and GC-MS analyses and confirmed the capability of the E-nose system, combined with chemometric methods, to differentiate pure sesame oil from commercially available sesame oil samples. These findings highlight the potential of E-nose technology as a rapid and cost-effective tool for sesame oil authenticity assessment and quality control.
For soft robotic systems to emulate the adaptive behaviors of natural organisms, integrated systems with both high compliance and multimodal sensing are essential. However, existing soft robotic designs often struggle to simultaneously achieve large deformability, high force output, and stable real-time multimodal perception. Here, we report a precompressed flexoskeleton soft actuator integrated with a self-powered flexible bimodal sensor for enhanced actuation and multimodal perception. The developed self-powered bimodal sensor enables the simultaneous detection of distance and pressure, thereby allowing soft robots to perceive both noncontact proximity information and contact stimuli with good operational stability and durability. To further support this sensing platform, a flexoskeleton derived from a trimmed spiral surface was incorporated into the actuator and assembled in a precompressed state, thereby constraining radial expansion and promoting deformation through the release of stored elastic energy. The actuator delivered a blocking force of 17.28 N at 70 kPa while maintaining a bending angle of 30.5°, and also supported multidirectional bending and modular assembly. The integrated bimodal sensor combines noncontact triboelectric proximity sensing with contact piezoelectric pressure sensing, enabling self-powered discrimination of approach and touch events. In the proximity sensing mode, the open-circuit voltage increases from 0.24 to 0.71 V as the distance decreases from 25 to 5 mm. In the pressure sensing mode, the device produces up to 5.98 V and 325 nA under an 80 N load, together with good operational durability. Benefiting from the synergistic integration of self-powered sensing and structural actuation, the system demonstrates adaptive interaction in serial manipulators, plant-inspired predatory grasping in parallel soft grippers, and programmable gait perception in tripedal soft robots. This work provides a feasible strategy for deeply integrating self-powered multimodal sensing with soft actuation and highlights the potential of functional sensing materials for intelligent soft robotic systems.
Solar radiation forecasting is a complex task since the radiation signal is nonlinear, intermittent and is significantly influenced by meteorological variability, which makes it vital for PV planning, renewable energy planning and stability of the smart grid. In this work, a replicable comparison between CNN-LSTM and CNN-BiLSTM models for one-step ahead solar clearness-index forecasting based on multivariate climate variables from NASA POWER dataset for Delhi, India, is presented. Under identical preprocessing, windowing, chronological splitting, and training conditions, CNN-LSTM achieved MAE = 0.0880, RMSE = 0.1100, R2 = 0.3100, EVS = 0.3154, WI = 0.6317, and APB = 1.89%, whereas CNN-BiLSTM obtained MAE = 0.1015, RMSE = 0.1224, R2 = 0.1456, EVS = 0.1998, WI = 0.5261, and APB = 5.98%. The Skill Scores shown and the negative values for direct clearness-index prediction do not imply that the persistence reference was unattainable, but rather reveal that the results are a controlled model-to-model comparison and not evidence of state-of-the-art superiority. Reconstructed all-sky irradiance produced stronger agreement with observations (MAE = 0.4353, RMSE = 0.5417, R2 = 0.7884, EVS = 0.7965, WI = 0.9299, and APB = 3.90%). The main task of CNN-LSTM is to provide a practical balance between accuracy and efficiency in this experimental context, and further testing with other locations, more powerful baselines and probabilistic forecasting techniques is needed.
Personalized healthcare is crucial for transforming medical practice, offering more precise, efficient, and patient-centerd care. It not only improves individual health outcomes but also enhances the overall efficiency of healthcare systems. Wearable and implantable devices have emerged as key technologies in this transformation, offering unprecedented opportunities for real-time physiological monitoring, early disease detection, and personalized health intervention. In parallel, the rapid advancement of artificial intelligence (AI) has further accelerated the integration of data-driven intelligence into healthcare systems, making interdisciplinary research at the intersection of AI, flexible electronics, and biomedical engineering increasingly important. However, the long-term deployment of intelligent wearable and implantable healthcare systems remains fundamentally constrained by power sustainability, mechanical flexibility, and the energy cost of on-device data processing. This review focuses on self-powered intelligence as an emerging paradigm for personalized healthcare, enabling continuous, long-term, and autonomous health monitoring beyond the limitations of battery-powered systems. By integrating flexible and smart electronics with low-power AI, self-powered intelligence provide a viable pathway toward intelligent, on-body and in-body healthcare platforms capable of real-time analysis and personalized health management.
Seawater zinc-air batteries (SZABs) stand out as promising candidates for marine and offshore energy supply. However, their practical implementation is greatly restricted by tardy oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) kinetics at the air cathode, severe chloride ion-induced catalyst corrosion, and structural deterioration of traditional binder-containing electrodes in seawater media. Herein, we design and fabricate a binder-free integrated electrode consisting of carbon-supported iron phthalocyanine- modified star-like cobalt sulfide arrays directly grown on nickel foam. The optimal catalyst (0.3FePc-C/CoS) integrates the respective advantages of Fe single atoms and cobalt sulfide, exhibiting excellent ORR and OER activity, delivering a prominent half-wave potential of 0.89 V versus RHE, and exhibiting a low OER overpotential of 160 mV at 50 mA cm-2 and robust stability in seawater. As a self-supported air cathode, the 0.3FePc-C/CoS-based battery attains a favorable open-circuit voltage reaching 1.48 V, prominent peak power density (126.4 mW cm-2), small charge-discharge potential polarization (0.52 V), excellent energy efficiency (68.8%) and extraordinary long-term cycling durability (>360 h). This work not only discloses a feasible synergistic modulation strategy for constructing high-performance bifunctional electrocatalysts but also provides a valuable reference for developing corrosion-resistant integrated air electrodes toward practical marine energy storage applications.
Leakage of corrosive hydrogen halides (HCl, HBr, HI) poses severe environmental and safety risks in industrial processes, necessitating high-performance sensor materials with balanced adsorption-desorption properties. Herein, we systematically investigate the hydrogen halide sensing performance of pristine and Ir/Mo/W-decorated InSe monolayers using first-principles calculations. All three transition metals form thermodynamically stable configurations on InSe with binding energies of - 3.233 eV (Ir), - 3.941 eV (Mo), and - 3.337 eV (W). Pristine InSe exhibits negligible interaction with hydrogen halides, with adsorption energies ranging from - 0.239 to - 0.377 eV. Metal modification dramatically enhances adsorption strength, reducing adsorption distances from 4.160 to 4.253 Å to 2.360-2.711 Å and inducing significant electronic response. Mo-InSe achieves an optimal balance for HBr and HI detection, with room-temperature recovery times of 1.14 and 5.88 s, respectively. W-InSe shows the strongest adsorption toward all three gases and achieves suitable recovery under moderate heating (12.93 s at 328 K for HCl, 13.27 s at 378 K for HBr, 12.94 s at 358 K for HI). This work demonstrates that metal-modified InSe monolayers offer a tunable platform for hydrogen halide sensing, covering both room-temperature reusable and heat-assisted recovery applications.
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, with diagnostic disparities, particularly pronounced in resource-constrained and decentralized healthcare settings. Recent advances in TinyML machine learning models optimized for ultra-low-power, memory-constrained embedded devices have created new opportunities for scalable on-device CRC screening and diagnostics. This review presents a systematic and CRC-centric analysis of TinyML technologies across the diagnostic continuum, including capsule endoscopy, histopathology, breath analysis, and biosignal-based screening. Unlike existing surveys that address TinyML from a general healthcare perspective, this study focuses specifically on the technical, clinical, and deployment challenges unique to CRC diagnostics. We propose a structured taxonomy encompassing model compression techniques, hardware-software co-design strategies, and clinical deployment paradigms, and critically analyze the accuracy-latency-energy trade-offs across representative platforms. This review further synthesizes recent (2024-2025) advances in TinyML compilers, hardware accelerators, and edge-cloud integration, highlighting their implications for real-world clinical translation. By consolidating current evidence, identifying benchmarking and regulatory gaps, and outlining a forward-looking research roadmap, this survey clarifies the role of TinyML as a viable enabler of real-time, privacy-preserving, resource-efficient CRC diagnostics. These findings provide actionable insights for researchers, clinicians, and system designers seeking to deploy TinyML solutions in equitable and clinically meaningful cancer care.
Aqueous zinc-ion hybrid supercapacitors (ZHSCs) have emerged as promising next-generation electrochemical energy-storage devices due to their intrinsic safety, low cost, and ability to combine the high energy density of batteries with the high power density and long lifespan of supercapacitors. However, development is still in its infancy, and challenges include the limited performance of the capacitive/pseudocapacitive cathode material, whose structure and chemistry largely determine the device's energy-storage capabilities. Carbon materials, particularly porous carbons, have been extensively studied as cathode materials in ZHSCs. Recent advancements-including activated carbons, template-assisted carbons, metal-organic-framework-derived carbons, and biomass-derived carbons-have enabled significant progress in tailoring pore structures, chemical functionalities, and electronic properties to enhance zinc-ion energy storage. This review summarizes these developments, examining their synthesis methods and the design principles shaping their electrochemical behavior. Additionally, strategies of heteroatom doping and hybridization were critically evaluated, highlighting pathways to enhance electronic/ionic conductivities and pseudocapacitance. Finally, remaining challenges-including limited tunability of pore structures, incomplete mechanistic understanding, and restricted performance-were outlined, along with future directions for rational design and scalable synthesis. This review offers an updated perspective on engineering carbon cathode materials for high-performing ZHSCs and their potential in advancing safe, sustainable electrochemical energy storage.
The interfacial defect challenge between perovskite and electron transport layer (ETL) in inverted perovskite solar cells have become a critical bottleneck for achieving concurrent high efficiency and stability in the process of industrialization. We developed a novel multifunctional integrated polymer semiconductor material P4N-Cl as an interface interlayer between perovskite and [6,6]-phenyl-C61-butyric acid methyl ester. Various functional groups including carbonyl group, Cl atom and sp2-N atom in the polymer backbone effectively passivate defects at the perovskite interface through a synergistic coordination mechanism and significantly suppress non-radiative recombination losses. Simultaneously, the robust interfacial binding at the heterointerface further optimizes the energy level alignment at the perovskite/ETL interface and enhances charge carrier dynamics. The inverted PSCs based on the P4N-Cl multifunctional layer achieved a champion efficiency of 26.20% and a high open-circuit voltage of 1.21 V. The target devices retained 96.2% and 90.2% of their initial power conversion efficiency after 2016 h aging in ambient air (40%-60% relative humidity) and 1500 h maximum power point tracking at 65°C under 1-sun illumination in nitrogen, respectively. This "one-stop" design provides exciting research prospects for constructing a new generation of commercially viable perovskite solar cells with high efficiency and long-term operation stability of devices.