Search in Publications
Found 6374 publications
Research progress in small-molecule donor-polymer acceptor organic photovoltaic cells
Publication Name: Organic Electronics
Publication Date: 2025-11-01
Volume: 146
Issue: Unknown
Page Range: Unknown
Description:
Organic solar cells (OSCs), characterized by their lightweight, flexibility, solution-processability for large-area fabrication, and low cost, exhibit significant complementary advantages to silicon-based photovoltaics, positioning them as a cutting-edge research frontier in clean energy. Among emerging architectures, small-molecule donor/polymer acceptor (SDPA)-based OSCs have attracted considerable attention due to their unique active layer stability, particularly their ability to maintain optimized phase-separated morphology under high-temperature conditions (>85 °C), offering potential to overcome the stability bottleneck in organic photovoltaic industrialization. However, the current record power conversion efficiency (PCE) of SDPA-OSCs remains at 12.1 %, significantly lagging behind mainstream bulk heterojunction systems (PCE >20 %). To advance the efficiency of SDPA-OSCs, extensive efforts have been devoted to optimizing materials, device engineering, and processing techniques. This review systematically summarizes recent progress in SDPA-OSCs from the perspectives of device architecture and active layer processing. Key focus areas include the impact of device structure engineering (conventional vs. inverted configurations) and active layer fabrication strategies (bulk heterojunction solution-coating and layer-by-layer deposition techniques) on charge carrier transport and device performance. By establishing robust “material structure–morphology–device performance” correlations, this work provides critical insights and technical references for developing high-efficiency SDPA-OSCs. Furthermore, future research directions and challenges in material innovation, morphology control, and scalable manufacturing are discussed to guide the advancement of SDPA-based organic photovoltaics.
Open Access: Yes
Approaches to studying wheat and maize drought stress responses
Publication Name: Plant and Soil
Publication Date: 2025-11-01
Volume: 516
Issue: 1
Page Range: 115-132
Description:
Introduction: Drought stress remains a critical challenge to sustainable agriculture worldwide, threatening crop productivity and food security. Understanding the physiological processes and defense mechanisms that crops employ under water-limited conditions is essential for developing strategies to enhance drought resilience. Plant responses to drought vary widely depending on species, genotype, developmental stage, and the severity and duration of stress. Beyond annual rainfall totals, yield is also shaped by seasonal precipitation patterns and related environmental factors, which influence the choice of cultivars and crop performance. Finding: This review examines the drought responses of maize and wheat, two globally important cereals, across morphological, physiological, biochemical, and molecular dimensions. Key responses include enhanced root development, reduced leaf area, stomatal regulation, decreased photosynthetic activity and water potential, and elevated proline and abscisic acid levels. Although varietal differences are noted, they are discussed only briefly. Water stress is commonly quantified via water potential and measured using tools such as the Scholander pressure chamber or the increasingly adopted ZIM probe, which allows non-destructive, continuous monitoring. While conventional breeding efforts have targeted drought tolerance, progress is constrained by these traits' polygenic and environmentally sensitive nature. Recently, biostimulants such as seaweed extracts and microalgae-based products have emerged as promising tools for enhancing stress tolerance. Conclusion: To meet the demands of a changing climate, future research should prioritize the integration of genetic, physiological, and biochemical strategies to develop crops with robust and durable drought resistance.
Open Access: Yes
Environmental Impacts of Synthetic Fuels †
Publication Name: Engineering Proceedings
Publication Date: 2025-11-01
Volume: 113
Issue: 1
Page Range: Unknown
Description:
In 2024, synthetic fuels regained attention as potential low-emission alternatives for internal combustion engines (ICEs), particularly in sectors where electrification remains challenging. This paper compares the estimated CO2 emission factors of fossil-based fuels and synthetic fuels blended with 20% bioethanol under standardized usage conditions. A key finding is that the emission factor of synthetic fuels is highly dependent on the carbon intensity of the electricity used to produce green hydrogen via electrolysis. Using the projected EU electricity mix for 2030, synthetic fuels show no clear advantage over fossil fuels. However, with a cleaner electricity mix expected by 2050, their emission factor becomes significantly lower. From an economic standpoint, the viability of synthetic fuel production largely depends on reducing green hydrogen costs of €1.50–2.00 per kg through technological advancements and large-scale deployment. This analysis offers a realistic perspective on when and how synthetic fuels could contribute to climate objectives and outlines the technical and economic conditions necessary for their environmental and market viability.
Open Access: Yes
Uncovering the dynamics of human-AI hybrid performance: A qualitative meta-analysis of empirical studies
Publication Name: International Journal of Human Computer Studies
Publication Date: 2025-11-01
Volume: 205
Issue: Unknown
Page Range: Unknown
Description:
Human-AI collaboration is an increasingly important area of research as AI systems are integrated into everyday workflows and moving beyond mere automation and augmentation to more collaborative roles. However, existing research often overlooks the dynamics and performance aspects of this interaction. Our study addresses this gap through a review of empirical AI studies from 2018–2024, focusing on the key factors influencing human-AI collaboration outcomes within the spectrum of Human-Centered Artificial Intelligence (HCAI). We identify 24 critical performance factors that influence hybrid performance, grouped into four categories using thematic analysis. Then, we uncover and analyze the complex, non-linear interdependencies between these factors. We present these relationships in a factor dependency graph, highlighting the most influential nodes. The graph and specific factor interactions supported by the papers reveal a quite complex web, an interconnectedness of factors. As opposed to being an easy-to-predict combination of inputs, human-AI collaboration in a given context likely leads to a dynamic, evolving system with often non-linear effects on its hybrid performance. Our findings and the previous research on automation technologies suggest that the application of AI tools in collaborative scenarios would benefit from a comprehensive performance framework. Our study intends to contribute to this future line of research with this initial framework.
Open Access: Yes
Comparative Analysis of Driving Performance and Visual and Physiological Responses Between Professional and Civilian Drivers in Simulated Environments
Publication Name: Applied Sciences Switzerland
Publication Date: 2025-11-01
Volume: 15
Issue: 22
Page Range: Unknown
Description:
Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary.
Open Access: Yes
DOI: 10.3390/app152212024
SHEAB: A Novel Automated Benchmarking Framework for Edge AI
Publication Name: Technologies
Publication Date: 2025-11-01
Volume: 13
Issue: 11
Page Range: Unknown
Description:
Edge computing is characterized by heterogeneous hardware, distributed deployment, and a need for on-site processing, which makes performance benchmarking challenging. This paper presents SHEAB (Scalable Heterogeneous Edge Automation Benchmarking), a novel framework designed to securely automate the benchmarking of Edge AI devices at scale. The proposed framework enables concurrent performance evaluation of multiple edge nodes, drastically reducing the time-to-deploy (TTD) for benchmarking tasks compared to traditional sequential methods. SHEAB’s architecture leverages containerized microservices for orchestration and result aggregation, integrated with multi-layer security (firewalls, VPN tunneling, and SSH) to ensure safe operation in untrusted network environments. We provide a detailed system design and workflow, including algorithmic pseudocode for the SHEAB process. A comprehensive comparative review of related work highlights how SHEAB advances the state-of-the-art in edge benchmarking through its combination of secure automation and scalability. We detail a real-world implementation on eleven heterogeneous edge devices, using a centralized 48-core server to coordinate benchmarks. Statistical analysis of the experimental results demonstrates a 43.74% reduction in total benchmarking time and a 1.78× speedup in benchmarking throughput using SHEAB, relative to conventional one-by-one benchmarking. We also present mathematical formulations for performance gain and discuss the implications of our results. The framework’s effectiveness is validated through the concurrent execution of standard benchmarking workloads on distributed edge nodes, with results stored in a central database for analysis. SHEAB thus represents a significant step toward efficient and reproducible Edge AI performance evaluation. Future work will extend the framework to broader workloads and further improve parallel efficiency.
Open Access: Yes
Explainable Machine Learning-Based Ground Motion Characterization: Evaluating the Role of Geotechnical Variabilities on Response Parameters
Publication Name: Geosciences Switzerland
Publication Date: 2025-11-01
Volume: 15
Issue: 11
Page Range: Unknown
Description:
Accounting for geotechnical property variability is crucial in seismic site response analysis. Traditionally, the influence of each geotechnical property on response parameters is assessed independently. However, this approach limits our understanding of the combined effects of multiple properties on ground response parameters. This study presents a novel, explainable machine learning (ML)-based approach to assess the influence of multiple geotechnical property variations on response parameters. Four ML models, namely AdaBoost, Extreme Gradient Boosting (XGBoost), Random Forest Regressor (RFR) and Gradient Boosting Machine (GBM), were developed for predictive models. The input factors were shear-wave velocity, plasticity index, soil thickness, input motion intensity and unit weight of the soils. The response parameters were peak ground acceleration (PGA) and peak ground displacement (PGD). Multiple statistical performance metrics were computed to evaluate the performance of the models. The results show the superior prediction performance of the GBM model with low error rates and high agreement index (AI), Kling–Gupta efficiency (KGE) and coefficient of determination (Formula presented.). The output of the GBM model was further analyzed using Shapley Additive exPlanation (SHAP) technique to explain and identify the most significant factors contributing to the predictions. Finally, the model was used to develop user-friendly web-based software to facilitate rapid predictions of PGA and PGD.
Open Access: Yes
INFLUENCE OF FFF PROCESS PARAMETERS ON THE MECHANICAL PROPERTIES OF SINTERED 17-4PH STAINLESS STEEL
Publication Name: Mm Science Journal
Publication Date: 2025-11-01
Volume: 2025-November
Issue: Unknown
Page Range: 8765-8772
Description:
This study examines how Fused Filament Fabrication (FFF) process parameters affect the mechanical properties of sintered 17-4PH stainless steel. Test specimens were printed from BASF Ultrafuse 17-4PH filament on an IDEX system and processed by industrial debinding and sintering. The effects of layer height, printing speed and infill angle were evaluated through tensile testing. The highest tensile strength of 802 MPa was achieved at 0.2 mm layer height, 25 mm/s printing speed and 45° infill orientation. Layer height showed the dominant influence on tensile strength, as later confirmed by ANOVA, while printing speed and infill angle had smaller or non-significant effects within the tested range. The results give a practical understanding of how printing parameters determine the mechanical behavior of 17-4PH parts and can support further optimization of metal FFF processes.
Open Access: Yes
Equation-oriented thermodynamic optimisation of heat pump integration in industrial heat recovery systems: A system-level pathway to cost and emission reduction
Publication Name: Energy
Publication Date: 2025-10-30
Volume: 335
Issue: Unknown
Page Range: Unknown
Description:
Integrating heat pumps into large-scale electricity-to-heat industrial processes has proven highly successful in enhancing the utilisation of renewable energy and contributing to carbon emission reductions. However, most studies focus on overall system performance, overlooking the detailed thermal behaviour of the heat pump itself. This limits the adaptability of heat pumps in dynamic industrial settings. This work proposes an equation-oriented framework that enables flexible integration of thermodynamically detailed heat pump models into industrial heat recovery systems. A superstructure-based optimisation model is developed to minimise energy costs and enhance efficiency, considering process constraints, network layout, and heat pump performance. The model dynamically optimises heat pump operation and placement to enhance waste heat recovery and overall system integration. Moreover, the approach supports the integration of low-grade utilities to further improve the energy efficiency. The proposed framework is validated through an industrial-scale case study of a crude oil distillation process. Life cycle assessment is conducted to quantify potential environmental and economic benefits. Results show that integrating heat pumps into the system recovered 50.52 % of low-pressure steam, reducing the total operating cost and annual cost by 12.88 % and 12.42 %. Additionally, total net carbon emissions decreased by 28.70 %. Lower electricity prices increase heat pump use and economic benefits but also amplify rebound effects. Furthermore, although high-temperature heat pumps operating above 150 °C tend to increase capital expenditures, they unlock greater energy efficiency, thereby accelerating the industrial decarbonisation process.
Open Access: Yes
