Bonwook Koo

Recent advances in large language models (LLMs) have produced impressive fluency, yet their application to specialized scientific domains like wood science remains limited. This study introduces WoodLLaMA, a domain-specific LLM fine-tuned on metadata from 16,929 wood science research articles, and examines the effects of fine-tuning and retrieval-augmented generation (RAG) on model performance. Evaluation utilized two datasets not included in the training data: a Journal question–answer (QA) set representing domain-specific expertise and a Wood Handbook QA set reflecting fundamental wood science knowledge. Using intrinsic metrics (perplexity) and QA-based metrics (cosine similarity, keyword matching, and BERTScores), along with qualitative case studies, fine-tuning was found to enhance linguistic fluency while RAG improved semantic alignment. Combining fine-tuning and RAG yielded the most robust and consistent performance. These results demonstrate the complementary value of fine-tuning and RAG for building domain-specific LLMs. The study offers a methodological framework for LLM evaluation and identifies future directions—such as leveraging full-text data, enabling multilingual support, integrating multimodal resources, and incorporating human-in-the-loop learning methods—for enhancing the performance and broadening the applicability of WoodLLaMA across a diverse range of domains.

Biochar has emerged as a promising strategy for carbon sequestration in the context of climate change and carbon neutrality goals. Among various feedstocks, underutilized forest biomass (UFB) holds significant potential for conversion into high-value carbon materials. However, the heterogeneity of UFB and the high cost of conventional analyses highlight the need for rapid prediction techniques for key carbon indicators, such as carbon content, atomic oxygen-to-carbon ratio, and atomic hydrogen-to-carbon ratio. This study proposes a chemometric model that non-destructively predicts the carbonization characteristics of biochar using attenuated total reflectance infrared (ATR-IR) spectroscopy combined with partial least squares regression (PLSR). Twenty biochar samples were produced from UFB at carbonization temperatures of 200 °C, 300 °C, and 400 °C. The ATR-IR spectra were preprocessed using normalization and second-derivative transformation before being used to construct the predictive models. The optimized PLSR models, which were validated through cross-validation and outlier removal, achieved high prediction accuracy for all three carbon indices (R² > 0.94). Variable importance in projection (VIP) analysis further identified the key spectral regions contributing to the model performance. These findings demonstrate that high predictive power and interpretability can be achieved without the use of complex machine learning algorithms, providing a practical analytical tool for assessing the quality of biochar and for the efficient utilization of forest residues.

A biomimetic synthetic approach converts a lignin monomer into a functional diphenoquinone that electrochemically captures and releases CO ₂ , offering a fully green route to renewable redox-active carbon-capture materials.