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Production-Ready Decomposition of Series Resistance Into Lateral and Fixed Components
Full text linkThe series resistance (Rs) of a solar cell is commonly treated as a constant, but this is a very poor approximation: in modern cells, Rs can vary by a factor of 4 depending on illumination, bias voltage, and temperature. The naïve model leads to incorrect characterization of cell parameters and misattribution of cell losses. Wagner’s linear-response series resistance (LR-Rs) model attempts to treat Rs rigorously but experimental confirmation of the model is limited to EL and PL measurements. In this work we validate the LR-Rs model using IV measurements at multiple light intensities. We find a hyperbolic dependence of Rs on diode current as predicted by Wagner. The LR-Rs model fits the data extraordinarily well, but with a much higher geometric dependency than predicted by Wagner. We show that the model allows us to decompose series resistance into lateral and fixed components. This decomposition is now available in all WAVELABS commercial LED flashers. We illustrate the immediate benefits of this improved model in commissioning of a heterojunction production line
Grouping Agri-Environmental Practices in Germany Along Behavioural Drivers for Adoption
Full text linkWidespread adoption of sustainable practices is crucial for a transformation of agriculture towards sustainability. Effective policy interventions to increase their adoption require an understanding of the behavioural drivers of adoption. Based on interviews with twelve experts on German agriculture, this study assesses the importance of a comprehensive set of behavioural factors for the adoption of 18 agri-environmental practices. The rated level of importance of the behavioural factors varies significantly across the practices. While enhancing farmers’ capability in terms of knowledge is confirmed to be a key factor for adoption of most practices, opportunity-related factors such as financial resources, and motivation-related factors such as future-orientation or habits are more specific to certain sub-groups of practices. Cluster analysis reveals three robust groups of practices that share similar behavioural drivers. For instance, a cluster consisting of agroforestry, hedges, and permanent grassland coincided as overall demanding for farmers and difficult to reverse, requiring future-orientation, a favourable legal context without short-term leases, and financial resources. For another cluster – legumes, reduced mowing, reduced tillage, rotational fallow and reduced soil pressure – adoption of the practices may actually save workforce and time for farmers, but - in addition to knowledge needs - depend on proper integration within farmers’ habits and routines. The findings of our study, both at the level of single practices and on cluster-level, can inform the design of targeted policy interventions such as trainings and financial support schemes
Investigation of Contact Properties and Device Performance for Bifacial Double-Side Textured Silicon Solar Cells With Polysilicon Based Passivating Contacts
Full text linkWe investigate the impact of the surface morphology on the contact properties of phosphorus doped poly-Si layers. If the poly-Si layer on a textured surface remains intact after high-temperature metallization using a fire-through (FT) silver (Ag) paste, the J0,metal is not expected to increase significantly while the specific contact resistivity can improve with a textured surface. The contact properties of the FT Ag contacts to n+ poly-Si deposited on both textured and planar surfaces are investigated by measuring and evaluating ρc and J0,metal. The reasons for differences in contact resistance and recombination are further investigated with SEM imaging. Solar cells with n-type polysilicon based passivating contacts on the rear side are fabricated and characterized. The scientific approach used, and the insights presented in this work, help to understand the mechanisms and behavior of screen-printed and fired-through contacts to polysilicon layers deposited onto textured silicon surfaces
Enhancing the Optical Performance of Mid-Infrared Chalcogenide Glass Through Liquid Coating
Full text linkLaser induced damage to optical component surfaces poses a critical challenge in the development of high-power laser systems. Such damage is typically influenced by its material host, defect distribution, laser parameters, and environmental conditions. The impact of surface defects becomes particularly pronounced under high-power, short-pulse laser irradiation, especially when surface roughness is suboptimal. In this study, we applied a liquid coating method to improve the surface quality of As2S3 glass under various surface conditions by eliminating defects with liquid. A detailed evaluation of the transmittance and surface laser damage threshold was conducted. The results reveal that effective liquid coating significantly reduces surface scattering and Fresnel reflection, resulting in up to a 24.8% increase in overall transmittance and a 2.17-fold improvement in the surface laser damage threshold. These findings underscore the critical role of liquid coating in enhancing optical material performance and offer a practical solution for optimizing high-power laser systems
Enhancing Olive Production in Mediterranean Agrivoltaic Systems: A Microclimatic Analysis Using Computational Fluid Dynamics Modeling: Insights From the “Borgo Monteruga” Project in Southern Italy
Full text linkAgrivoltaic Systems (AVs), combining agriculture and solar power generation, represent a sustainable land use method. This research is conducted for the “Borgo Monteruga” agrivoltaic project, located in Southern Italy. The project is characterized by the inter-cropping of high-density olive groves, arranged in hedgerows between solar trackers, with fodder crops on either side or, alternatively, medicinal crops, depending on the soil quality. The park boasts a peak power capacity of 291.33 MWp, achieved through the installation of double-sided 600 W photovoltaic modules and a 50 MW storage system. The novelty of this project lies in the proactive assessment of microclimatic parameters to optimize the AVs layout, aiming to reduce shading on crops and enhance the efficiency of both agricultural and solar energy production. The photovoltaics modules in AVs significantly impacts the local microclimate, influencing aspects such as solar radiation, air and soil temperatures, wind speed, and groundwater retention. Understanding these microclimatic shifts is essential not only for the effective management of AVs and strategic crop selection but also for choosing optimal adaptive solutions to climate change. Additionally, this knowledge is key to establishing a tailored monitoring system with innovative and targeted strategies, ensuring the AVs resilience and productivity in the face of evolving environmental conditions. This study sets a new benchmark in AVs design and optimization by implementing advanced computational fluid dynamics (CFD) modeling for its microclimatic analysis, thereby contributing significantly to the field of sustainable agricultural practices
A Comprehensive Life Cycle Assessment Framework for Agrivoltaic Systems
Full text linkThis paper suggests a comprehensive life cycle assessment (LCA) framework for agrivoltaic systems - systems that integrate photovoltaic energy and agricultural production. Agrivoltaics proposes a solution that provides additional land for meeting renewable energy goals while supporting agricultural production and water and land use efficiency. This study contributes to the assessment of the dynamic life cycle improvements associated with agrivoltaics installations through applying the framework to a case study at Jack\u27s Solar Garden in Boulder, Colorado. We utilize the ReCIPe midpoint hierarchist method and leverage data from existing OpenLCA databases to evaluate the environmental and economic impacts of co-located crop production and solar energy generation. The results highlight the potential for reduced land and water use, underscoring agrivoltaics\u27 role in managing land conflicts and water scarce environments. However, challenges such as increased material demands, limited open-source data, and the need for tailored configurations remain. Our framework aims to guide researchers, producers, and policymakers in making informed decisions on the adoption of agrivoltaics
CUET_Zenith at LLMs4OL 2025 Task C: Hybrid Embedding-LLM Architectures for Taxonomy Discovery
Full text linkTaxonomy discovery, the identification of hierarchical relationships within ontological structures, constitutes a foundational challenge in ontology learning. Our submission to the LLMs4OL 2025 challenge, employing hybrid architectures to address this task across both biomedical (Subtask C1: OBI) and general-purpose (Subtask C5: SchemaOrg) knowledge domains. For C1, we have integrated semantic clustering of Sentence-BERT embeddings with few-shot prompting using Qwen-3 (14B), enabling domain-specific hierarchy induction without task-specific fine-tuning. For C5, we have introduced a cascaded validation framework, harmonizing deep semantic representations from sentence transformer {all-mpnet-base-v2}, ensemble classification via XGBoost, and a hierarchical LLM-based reasoning pipeline utilizing TinyLlama and GPT-4o. To address inherent class imbalances, we have employed SMOTE-based augmentation and gated inference thresholds. Empirical results demonstrate that our hybrid methodology achieves competitive performance, confirming that the judicious integration of classical machine learning with large language models yields efficient and scalable solutions for ontology structure induction. Code implementations are publicly available
ELLMO at LLMs4OL 2025 Tasks A and D: LLM-Based Term, Type, and Relationship Extraction
Full text linkThis paper presents an approach to building ontologies using Large Language Models (LLMs), addressing the need in many domains for quality knowledge data extraction from vast stores of text data. In particular, we focus on extracting terms and types from text and discovering relationships between types. This work was completed as part of the 2025 LLMs4OL Challenge, where quality training and testing data, as well as several defined tasks were provided. Many teams competed to produce the best output data across many domains. Our methodology involved prompt engineering, classification, clustering, and vector databases. For the first task, discovering terms and types, we used two methods, (1) directly tailoring prompts to find the terms and types separately and (2) an approach that discovered terms and types simultaneously and then classified them afterwards. For discovering relationships, we used clustering and vector databases to attempt to reduce the number of potential edges; then, we queried the LLM for probabilities for each of the potential edges. While our findings indicate promising results, further work is necessary to address challenges related to processing large datasets, particularly in optimizing efficiency and accuracy
Unmediated AI-Assisted Scholarly Citations
Full text linkTraditional bibliography databases require users to navigate search forms and manually copy citation data. Language models offer an alternative: a natural-language interface where researchers can write text with informal citation fragments and have them automatically resolved to proper references. However, language models generate fabricated (hallucinated) citations at substantial rates, making them unreliable for scholarly work. We present an architectural approach that combines the natural language interface of LLM chatbots with the accuracy of direct database access, implemented through the Model Context Protocol. Our system enables language models to search bibliographic databases, perform fuzzy matching, and export verified entries, all through conversational interaction. A key architectural principle bypasses the language model during final data export by fetching entries directly from authoritative sources, with timeout protection, to guarantee accuracy. We demonstrate this approach with MCP-DBLP, a server providing access to the DBLP computer science bibliography. The system transforms form-based bibliographic services into conversational assistants that maintain scholarly integrity. This architecture is adaptable to other bibliographic databases and scholarly data sources
Enhancing the Thermal Efficiency of Longitudinally Finned Parabolic Trough Solar Receivers
Full text linkThe objective of this study was to investigate the conjugate heat transfer in a longitudinally finned parabolic trough solar thermal receiver tube, taking into account the non-uniform heat flux distribution along the tube circumference. Both smooth and longitudinally finned stainless steel receiver tubes, with Syltherm 800 oil as the heat transfer fluid, were numerically simulated using Ansys Fluent 2023R2 with periodic boundary conditions for Reynolds numbers ranging from 20,000 to 60,000. Two fin configurations were investigated in the study: two fins - of little practical interest but very useful to gain physical insight into the problem - and six fin receiver tubes. In addition, sub-cases within the two- and six-fin configurations were analyzed by varying the position and height of the fins. It was found that fins on the bottom side, exposed to a higher heat flux, mainly enhanced conduction, while fins on the top side mainly enhanced convection by directing higher velocity values toward the bottom side. To reduce hot spots and improve thermal efficiency, fins should be located in higher heat flux areas, and shorter fins on the bottom side and longer fins on the top side are recommended. Finally, the performance evaluation criteria were assessed using both the inner Nusselt number, based on the convective heat transfer coefficient as commonly reported in the literature, and an overall Nusselt number, based on the total heat transfer resistance of the receiver. For the most favorable two-fin and six-fin cases, the maximum temperature in the receiver decreased by a maximum of 16.7 K and 30 K, respectively