Search Results (169)

Photovoltaic–thermal (PVT) collectors co-generate electricity and heat—natural front ends for thermochemical energy storage (TCES)—provided the heat transfer fluid stays above the reactor’s desorption temperature. Using an eight-node model of a 0.6834 m² collector at 8.64° N whose thermal core is partially validated against measured data from the same tropical–coastal site (rooftop PV module temperature, RMSE 3.8 °C; prototype absorber-to-water heat transfer, RMSE 1.3 °C), flow-regulated to the ≈95 °C SrCl₂/NH₃ desorption threshold, we quantify the energy and exergy delivered for charging under tropical–monsoon intermittency. The 95 °C setpoint operation, the ≈5.3 h charging window, and all reported exergy yields are simulated: the built prototype delivered hot water peaking at 79 °C and did not reach the 95 °C setpoint. On a measured clear-sky day (clearness index K_t = 0.52), the collector yields 1.38 kWh of energy but only 0.43 kWh of exergy (first-law efficiency ≈ 38%; gross exergy efficiency ≈ 13%); across a 30-seed synthetic-intermittency ensemble, the exergy yield is 0.678 kWh at ≈14% gross exergy efficiency (≈52% combined first-law efficiency). In both cases, the thermal stream dominates the energy output while the electrical stream dominates the exergy output—on the sunlit day, the exergy is about 80% electrical—because 95 °C heat carries a Carnot factor (exergetic quality factor, 1 − T_a/T₇, at the instantaneous ambient dead state) of only ≈0.18 and an integrated Bejan/Kotas thermal-exergy quality of only ≈0.09. The controller holds the outlet within 1.4 K of the setpoint for ≈5.3 h, whereas no fixed flow in the 0.5–5.0 L min⁻¹ range ever reaches it: feedback control is a structural enabler, not an optimisation. On overcast days, the threshold is never reached and charging heat collapses to zero, leaving a PV-only generator. Exergy delivery is nonetheless nearly controller-independent: the accumulated exergy delivery deficit after a 50% irradiance drop is 937 kJ, a controller-independent value changing only 1.3% across a systematic 4 × 4 gain sweep (K_p 0.33–2.7×, K_d 0.25–5× of nominal), and predictive control improves it by ≤1%. For PVT–TCES at this scale, the decisive lever is deployability, not control sophistication. Full article

Decarbonizing industrial heating in cold climates remains challenging due to high thermal demand and strong seasonal variability. While the existing literature predominantly relies on steady-state or isolated component analyses, this study introduces a novel, multi-scale dynamic modeling framework. This framework evaluates the annual transient performance of an integrated renewable architecture. The proposed system couples a building-envelope Transpired Solar Collector (TSC) with a series-connected Photovoltaic Thermal/Solar Thermal (PVT-ST) array. Computational Fluid Dynamics (CFD) is employed to resolve the localized convective heat transfer within the TSC. Subsequently, a data-driven clustering methodology scales these transient results into a comprehensive annual system-level simulation featuring sensible Thermal Energy Storage (TES). The results demonstrate robust performance under Canadian winter conditions. The TSC maintains stable thermal efficiencies between 50% and 60%, peaking at over 64%. Annually, the integrated dual-source system delivers 229.7 MWh of useful thermal energy to offset primary fossil fuel consumption. Furthermore, the analysis identifies 128.76 MWh of seasonal surplus capacity. This underscores the critical necessity of dynamic TES integration. Ultimately, this framework establishes a highly defensible, predictive methodology for designing and implementing synergistic solar thermal networks for industrial decarbonization. Full article

The practical performance of building-integrated solar systems in cold climates is strongly governed by temperature-grade matching between solar energy output and space-heating demand. However, year-round field evidence comparing solar thermal collectors, photovoltaic systems, and photovoltaic/thermal systems under the same building, climatic, and heating-network boundary conditions remains limited. This study conducted a year-round field evaluation of solar collector (SC), photovoltaic (PV), and photovoltaic/thermal (PVT) systems installed in an office building in Tianjin, China. Continuous operating data collected from November 2022 to October 2023 were used to assess seasonal thermal output, electricity generation, effective heat supply, solar utilization efficiency, carbon reduction, and payback period. During the heating season, SC exhibited the strongest direct-heating capability among the investigated systems, delivering 817.50 MJ/m² of useful heat. In contrast, under the investigated system configuration without heat-pump assistance, the outlet temperature of the PVT subsystem remained below the 45 °C direct-heating threshold, and its thermal output could not be directly utilized for winter space heating. This result is specific to the investigated operating conditions and does not exclude the potential application of PVT systems coupled with heat pumps or low-temperature heating terminals. During the non-heating season, the investigated PVT subsystem simultaneously produced electricity and usable low-temperature heat, with heat and electricity accounting for 61.3% and 38.7% of its useful output, respectively, indicating its potential for combined energy harvesting. Under the investigated climatic, system, cost, and energy-demand conditions, the entropy-weighted TOPSIS assessment ranked SC highest when non-heating-season heat demand was present, whereas PV was more suitable when such heat demand was absent. Furthermore, a demand–output matching method was developed to support SC/PV area allocation for different building types. Under the investigated climatic and energy-demand assumptions, the recommended PV area ratios were 54.5%, 67.4%, and 79.7% for residential, office, and commercial buildings, respectively. These results provide field evidence for effective heat evaluation, temperature-grade matching, and component selection in solar-assisted heating systems for cold-climate buildings. Full article

This study presents a technical assessment of solar energy systems for integrated agricultural use and rural electrification. A model village comprising 30 households was considered, and high-resolution hourly load profiles were developed to characterize consumption dynamics, including peak demand and sectoral distribution across residential, agricultural, public, healthcare, and commercial users. A 60 kW photovoltaic (PV) system was designed in conjunction with an independent solar thermal installation for hot water supply. The system configuration was established through component sizing and numerical modeling, incorporating heat transfer mechanisms and operational constraints. Time-dependent simulations performed in MATLAB (R2022b) evaluated PV power output, battery storage cycling, and thermal system performance over a 24-h horizon. A comparative analysis of standalone PV, hybrid PV/T, and decoupled PV–thermal configurations was conducted based on performance and operational criteria. The results indicate that separated electrical and thermal subsystems achieve improved cost-effectiveness, enhanced reliability, and reduced maintenance requirements. The proposed approach demonstrates the technical viability of solar-based energy systems for rural applications, supporting energy autonomy, reduced fossil fuel dependence, and sustainable agricultural development. Full article

The ongoing global energy transition has intensified the need for precise modeling of renewable energy systems, especially photovoltaic–thermal (PV/T) systems that have the ability to produce both electrical and thermal energy. Improving the efficiency and reliability of PV/T systems is a key enabler of the transition toward sustainable energy. Accurate forecasting of their thermal performance is therefore essential to maximize renewable energy use and reduce energy losses. A deep learning-based method is proposed in this study for the prediction of the thermal efficiency of an air-based PV/T system. More specifically, temporal deep learning architectures are investigated to exploit the complex nonlinear relationships and temporal dependencies governing the thermal behavior of the PV/T collector. A comprehensive comparative analysis is conducted using four state-of-the-art architectures, namely Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Transformer. Furthermore, the influence of sequence length is examined through a sensitivity analysis considering forecasting horizons of 1 h, 6 h, 12 h, and 24 h. The models are evaluated using the Coefficient of Determination (R²), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The results demonstrate that forecasting performance is strongly influenced by the selected temporal horizon. Among the investigated configurations, the 24-h horizon provided the most informative temporal context for thermal efficiency prediction. Under this common forecasting horizon, the LSTM model achieved the highest predictive accuracy, reaching an R² of 0.9952, an RMSE of 0.5975, and an MAE of 0.2364, outperforming the TCN, GRU, and Transformer architectures. The residual error and convergence analyses further highlighted the effectiveness of recurrent neural networks in capturing the thermal dynamics of the investigated PV/T system. By enabling accurate and reliable thermal efficiency forecasting, the proposed framework supports improved energy management, higher energy efficiency, and a stronger integration of renewable energy systems, thus contributing to more sustainable operation of hybrid solar energy technologies. Full article

Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures. Full article

Nanofluid-based spectral filtering offers a promising approach to enhance photovoltaic/thermal (PV/T) system performance by utilizing the full solar spectrum. However, system optimization remains challenging due to complex nonlinear relationships between nanofluid parameters and overall performance. This study develops a prediction-optimization framework integrating deep neural networks (DNN) with genetic algorithms (GA) to accurately analyze multi-parameter interactions and achieve globally optimal designs for nanofluid-based PV/T systems. High-throughput datasets for three nanofluids (Ag, Au, Al) were constructed using theoretical calculations that combined Lorentz–Mie theory, Monte Carlo simulations, and a coupled opto-electro-thermal model. Three machine learning models—DNN, random forest (RF), and decision tree (DT)—were employed to predict key PV/T performance parameters. By synergizing machine learning with GA, a closed-loop prediction-optimization process was established to efficiently identify optimal design parameters. Among the models evaluated, the DNN demonstrated superior performance, achieving prediction accuracies above 99.48% for all three key performance indicators (η_pv, η_th, and MF), significantly outperforming the RF and DT models. Furthermore, SHAP analysis was conducted to quantify the contribution of each input feature and enhance model interpretability. Coupled with the GA, the DNN-GA framework successfully identified globally optimal design parameters for each nanofluid. For instance, for Ag nanofluid, the optimal combination (r = 4.02 nm, h = 9.91 mm, f_v = 9.45 × 10⁻⁵) yielded a maximum MF value of 1.3603. This work presents an innovative machine learning framework for designing nanofluid filters in PV/T systems, which reduces reliance on iterative experimentation and accelerates the development of high-performance solar energy systems, demonstrating practical value. Full article

Passive components in integrated circuits occupy significant areas and increase production costs, driving the demand for compact alternatives. This study presents a generalized, electronically controllable positive/negative floating impedance multiplier implemented in TSMC 180 nm CMOS technology. To achieve a compact layout, the architecture utilizes custom-designed operational transconductance amplifiers (OTAs). The circuit operates on a lossless principle, scaling resistance, capacitance, and inductance values within a wide multiplication range of −100 to +100 using only a single base element. Comprehensive LTspice simulations including PVT, Monte Carlo, THD, and noise analyses verify the design’s stable operation, low distortion, and favorable noise characteristics across various filter configurations. Furthermore, practical feasibility is validated through SPICE simulations using commercial LM13700 OTA, confirming consistent behavior for real-world applications. The proposed active topology occupies a compact core area of only 5831 μm². By scaling down large passive components, this design decreases the overall system-level footprint, providing a versatile and area-efficient solution for tunable analog IC applications. It should be noted that the reported 5831 μm² corresponds to the active core only, while the effective system-level area benefit depends on the selected base impedance and the target application. Full article

In Brazil, more than 80% of households rely on electricity for water heating, representing approximately 13% of residential electricity consumption and significantly contributing to peak grid demand. As a prominent alternative for supplying household thermal energy and reducing grid stress, this study experimentally evaluates, under tropical climate conditions, the performance of a modular water-based photovoltaic–thermal (PVT) system and compares it with a conventional photovoltaic (PV) system operating simultaneously under identical environmental conditions. The PVT system, based on commercial PV modules coupled to roll-bond heat exchangers, a storage tank, and a shower outlet, was tested under three hydraulic regimes: natural thermosiphon, closed-loop, and Forced circulation. A dedicated ESP32-based data acquisition system, integrated with a cloud platform, continuously monitors electrical, thermal, and meteorological variables. Results show that PVT modules exhibit a small electrical efficiency reduction due to increased cell temperatures, which is largely compensated by the simultaneous thermal generation, yielding overall efficiency gains of 74.04%, 76.53%, and 7.62% over the reference PV system for Normal, Forced, and Closed circulation, respectively. The comparative analysis identifies Forced-circulation scheduling and the matching between thermal generation and consumption as key factors for performance optimization. The findings provide practical guidelines for deploying PVT systems to replace electric showers in tropical regions, reducing residential electricity consumption and mitigating peak-demand stress on the grid. Full article

A dual-mode variable gain amplifier (VGA) with a wide-dynamic-range is proposed in this paper. The VGA is designed in a 0.18 μm CMOS process, and it has a body-driven variable load cell and binary gain array structure to implement both the digitally stepped programmable gain amplifier (PGA) mode and the analog-controlled VGA mode. This design removes additional digital conversion modules when integrated into an automatic gain control (AGC) loop, which simplifies the whole system architecture significantly. The design is also able to address several limitations of conventional VGAs, such as a single control mode, low AGC compatibility, and a narrow gain range. The simulation results after post-layout indicate that at PGA mode, the design has an ultra-wide gain band of −0.03 to 126.9 dB with a constant gain step of 1 dB. And in VGA mode, it allows smooth, continuous gain adjustment over a large range of −25.3 dB to 187.4 dB. The bandwidth of −3 dB is more than 45 MHz in both modes. The whole VGA uses 1.026 mW and has a core size of 0.011 mm². The output 1-dB compression point (OP1dB) was −1.57 dBm at minimum gain in the PGA mode and −4.02 dBm in the VGA mode. Besides, PVT analysis, Monte Carlo simulations and AGC system-level verification are evident enough to prove that the suggested VGA has high immunity to PVT (Process, Voltage, Temperature) variations, stable processes and high practicality in engineering applications. Full article

This study explores a photovoltaic/thermal (PV/T)-based electrolysis system designed for dual production of hydrogen fuel and domestic hot water (DHW), providing a sustainable energy solution amid rising global emissions. A dynamic rule-based control mechanism with hysteresis thresholds on hydrogen-storage state of charge (SoC) is implemented to balance electrolyzer operation with intermittent solar availability, maintaining PV/T power outputs while preventing storage overfilling and minimizing start–stop cycling. The system is assessed across 27 geographically diverse cities spanning a wide range of solar irradiation and energy price structures. Annual hydrogen yields range from 20 kg/yr in high-latitude locations (Helsinki, Stockholm) to 33.5 kg/yr in high-irradiation regions (Riyadh, Abu Dhabi), while the levelized cost of hydrogen (LCOH) spans from 6.47 USD/kg (Riyadh) to 22.86 USD/kg (Helsinki). Economically, the system achieves its strongest performance in solar-rich, high-energy-cost environments: Rome records the highest net annual cash flow (858.9 USD/yr) and shortest payback period (2.47 years), followed by Davos, Madrid, Brasília, and Canberra. In contrast, locations with subsidized energy tariffs—such as Algiers, Kyiv, and Tehran—yield low or negative net cash flows, rendering the system economically unviable without policy support. Environmental analysis reveals annual CO₂ avoidance ranging from 0.33 ton/yr (Stockholm) to 2.97 ton/yr (Riyadh), with a global mean of 1.095 ton/yr and a combined total of approximately 29.6 tons/yr across all examined sites. A machine learning model is developed to generalize performance predictions across unseen locations, achieving leave-one-out (LOO) R² values of 0.953 (net cash flow), 0.935 (LCOH), and 0.947 (LCO-DHW), with mean absolute errors below ±1 USD/kg and ±0.03 USD/kWh. The findings confirm that, under fixed capital cost assumptions, local electricity price and solar irradiation are the dominant drivers of economic viability, while grid carbon intensity and solar resource jointly govern environmental performance, with markets offering irradiation above 1500 kWh/m²·yr and electricity prices exceeding 0.2 USD/kWh representing the most promising deployment targets. Full article

Background/Objectives: Portal vein thrombosis (PVT) is a clinically significant condition in which early risk stratification remains challenging, particularly in emergency settings where rapid decision-making is required. This study aimed to evaluate the prognostic value of the Systemic Immune-Inflammation Index (SII), Systemic Inflammation Response Index (SIRI), and the Model for End-Stage Liver Disease (MELD) score in predicting the need for intensive care in patients with PVT. Methods: A retrospective analysis was conducted on adult patients (>18 years) diagnosed with PVT in the emergency department between January 2018 and December 2024. A total of 29 patients meeting the inclusion and exclusion criteria were included. Demographic characteristics, laboratory parameters, Intensive Care Unit (ICU) admission status, and 90-day mortality were analyzed. The sensitivity and specificity of MELD, SII, and SIRI for predicting ICU admission were calculated. Non-normally distributed variables were expressed as median (interquartile range, IQR) and compared using the Mann–Whitney U test. Results: The mean age of patients was 60.5 ± 16.2 years, and 18/29 (62.1%) were male. ICU admission was required in 9/29 (31.0%) of cases. MELD score (median 18.7 [11.0–21.9] vs. 7.9 [6.7–13.5], p = 0.003), bilirubin (median 2.4 [1.0–4.2] vs. 0.7 [0.4–1.1], p = 0.016), and SIRI (median 6.4 [2.3–21.3] vs. 1.4 [0.6–9.3], p = 0.038) were significantly higher in ICU-admitted patients. MELD score showed 66.7% sensitivity and 95% specificity, while SIRI had 88.9% sensitivity and 55% specificity for ICU prediction. Conclusions: MELD score, bilirubin, and SIRI are significantly associated with ICU admission in PVT patients. Their integration into emergency department protocols may assist in early risk stratification and resource allocation. Full article

This paper presents a nanowatt-scale operational transconductance amplifier (OTA) and an electronically tunable third-order low-pass filter (LPF) designed for energy-constrained biomedical signal conditioning. The circuits are implemented in a 65 nm CMOS process and verified through comprehensive schematic-level simulations. Biased in the deep subthreshold region at 1 nA, the OTA achieves a 50 dB low-frequency gain, a 225 Hz unity-gain bandwidth at 10 pF load capacitance and an input-referred noise floor of 1.55 μV/√Hz, with a total power consumption of only 1.75 nW. The integrated third-order LPF provides a wide tuning range (37–668 Hz) via bias current modulation, exhibiting excellent linearity with a THD of 0.059% and a 65.3 dB dynamic range. Monte Carlo and PVT corner analyses demonstrate the design’s theoretical robustness against process variations and environmental fluctuations. ECG signal simulations validate the circuit’s effectiveness in suppressing high-frequency artifacts while preserving morphological integrity, providing a proof-of-concept for ultra-low-power wearable healthcare architectures. Full article

Decarbonizing the transportation sector requires quick adoption of low-carbon energy carriers, with green hydrogen becoming a promising option for zero/low-emission mobility. Hydrogen refueling stations powered by renewable energy sources present a practical way to cut down lifecycle greenhouse gases and ease grid congestion. Nonetheless, most existing photovoltaic (PV)-based hydrogen production systems focus solely on electrical aspects, overlooking thermal energy flows and temperature effects that greatly impact PV and Electrolyzer performance. This study provides a thorough techno-economic evaluation of a hybrid PV/photovoltaic-thermal (PVT) green hydrogen system for refueling stations. The simulation framework models the combined electrical, thermal, and hydrogen subsystems under realistic conditions, incorporating rooftop PV/PVT collectors, battery storage, a water Electrolyzer, and hydrogen storage. Thermal energy from the PVT is used to pre-heat Electrolyzer feedwater, lowering electricity demand for hydrogen production and boosting PV efficiency via active cooling. Hydrogen production follows a demand-driven control strategy based on randomly generated stochastic daily refueling events. Three configurations are compared: (i) grid-only electrolysis, (ii) PV-only assisted electrolysis, and (iii) fully integrated PV/PVT-assisted electrolysis. The results show that the integrated PV/PVT setup significantly increases self-consumption, autarky rate, and overall efficiency, while lowering reliance on grid electricity and hydrogen production costs. Developed case studies highlight the economic feasibility and real-world viability of PV/PVT-assisted (decentralized) hydrogen refueling infrastructure. Full article

The global incidence of skin cancer is rising, making it an increasingly critical public health issue. Malignant skin tumors such as melanoma originate from pathological alterations in skin cells, and their accurate early-stage segmentation is crucial for quantitative analysis, early diagnosis, and effective treatment. However, achieving precise and efficient segmentation remains a major challenge, as existing methods often struggle to capture complex lesion characteristics. To address this challenge, we propose a novel deep learning framework that integrates the PVT v2 backbone with two key modules: the Spatial-Aware Feature Enhancement (SAFE) module and the Multiscale Dual Cross-attention Fusion (MDCF) module. The SAFE module enhances multi-scale encoder features through a dual-branch architecture, which adaptively extracts offset information to integrate fine-grained shallow details with deep semantic information, thereby bridging the feature gap across network depths. The MDCF module establishes bidirectional cross-attention between decoder and encoder features, followed by multi-scale deformable convolutions that capture lesion boundaries and small fragments across heterogeneous receptive fields, thereby enriching semantic details while suppressing background interference. The proposed model was evaluated on two public benchmark datasets (ISIC 2016 and ISIC 2018), achieving Intersection over Union (IoU) scores of 87.33% and 83.67%, respectively. These results demonstrate superior performance compared to current state-of-the-art methods and indicate that our framework significantly enhances skin lesion image analysis, offering a promising tool for improving early detection of skin cancer. Full article