The Repository digital repository system captures, stores, indexes, preserves, and distributes digital research material. http://scholar.dgist.ac.kr:80 2026-06-15T20:39:01Z 2026-06-15T20:39:01Z Santos, Katrina Wi, Tae-Ung Wang, Haotian Hicks, Andrea https://scholar.dgist.ac.kr/handle/20.500.11750/60418 2026-06-15T07:10:13Z 2026-06-30T15:00:00Z

Title: Life cycle assessment of a novel porous solid-state electrolyte reactor: Acetic acid as a case study Author(s): Santos, Katrina; Wi, Tae-Ung; Wang, Haotian; Hicks, Andrea Abstract: Utilizing captured waste carbon dioxide (CO2) emissions as feedstocks to carbon monoxide (CO) electrolyzers is a promising technology to produce valuable chemical products at an industrially relevant scale. The chemical industry is currently operated by industrial processes that are heavily reliant on fossil-based feedstocks making it a hard to decarbonize industry but also simultaneously making it an industry that is ideal for captured carbon valorization. Through the use of a CO reduction reaction (CORR) utilizing a porous solid-state electrolyte (PSE) reactor, high-purity chemical products can be produced from captured waste CO2 emissions after upstream processing and a tandem CO2 reduction reaction (CO2RR) to convert the captured CO2 into a CO feedstock for the PSE reactor. This work presents a life cycle assessment (LCA) and preliminary techno-economic assessment (TEA) of the conventional CORR electrolysis process utilizing a PSE reactor to produce high-purity acetic acid. The production of acetic acid, a high-demand commodity chemical, is utilized in this case study to showcase the PSE CORR reactor capabilities which are unique to its field in that a PSE reactor forgoes the requirement for energy intensive downstream product separation efforts associated with conventional liquid-based electrolytic reactors. Through LCA methodology, this work shows that CORR electrolysis utilizing a PSE can potentially reduce CO2 eq emissions per kg of acetic acid produced by at least 93% or more as compared to the traditional production process of methanol carbonylation and promote a near carbon neutral alternative process to industrially producing acetic acid when utilizing captured carbon with any energy grid excluding ones solely operated by coal and even potentially promote a carbon negative process under certain clean energy sources. This conclusion applies when the carbon feedstock is considered a waste stream and the “zero-burden assumption” is applied to all upstream processing except includes the upstream energy required to capture and convert the CO2 into CO. When not utilizing captured carbon, the process is still near carbon neutral even without the captured carbon credit for all grids excluding those operated exclusively using coal and far outperforms the traditional production method of methanol carbonylation regardless of the electricity source. However, this scenario does not currently show carbon-negative production of acetic acid even through the use of renewable energy. © 2026 Elsevier B.V.

2026-06-30T15:00:00Z Park, Jun Hyun Park, Jae Woo Choi, Min Seok Pang, Sang Uk Choe, Jun Seok Yu, Tae Sang Jang, Kyung-In Kim, Jin-Tae Chung, Ha Uk Kim, Jang Hwan Kim, Bong Hoon https://scholar.dgist.ac.kr/handle/20.500.11750/60417 2026-06-15T18:01:16Z 2026-02-28T15:00:00Z

Title: Multifunctional Sweat Sensors Using Semiconductor Fibers Based on Two-Dimensional Nanomaterials Author(s): Park, Jun Hyun; Park, Jae Woo; Choi, Min Seok; Pang, Sang Uk; Choe, Jun Seok; Yu, Tae Sang; Jang, Kyung-In; Kim, Jin-Tae; Chung, Ha Uk; Kim, Jang Hwan; Kim, Bong Hoon Abstract: Sweat monitoring offers real-time insights into physiological conditions such as hydration, muscle fatigue, and metabolic status. However, conventional sweat sensors often face challenges associated with unstable skin contact and insufficient sampling. In this study, a fiber-based wearable sensing platform is proposed, which incorporates semiconducting molybdenum disulfide (MoS2) and polylactic acid (PLA) composite fibers fabricated via wet spinning. By exploiting the high surface-to-volume ratio and n-type semiconducting nature of the MoS2 network, the sensor selectively detects major biomarkers including electrolytes (Na+ and K+) and metabolites (lactic acid and NH4+) via distinct electrostatic screening and charge trapping mechanisms. Furthermore, the intrinsic capillary action and thermal insulation of the fibers ensured reliable sweat collection without the requirement for external power. Additionally, the composite fiber exhibits piezoresistive capabilities, enabling simultaneous pressure monitoring to track physical motion. Multifunctional sensing facilitates the early diagnosis of metabolic disorders and the precise tracking of athletic performance. The developed fiber-based sensor provides a robust textile-integrated solution for next-generation personalized healthcare monitoring. © 2026 The Author(s). Small Structures published by Wiley-VCH GmbH.

2026-02-28T15:00:00Z Won, Chihyeong Cho, Young Uk Kweon, Siyeon Cho, Sungjoon Kwon, Chaebeen Kim, Hyun Woo Lee, Ju Young Park, Sang Hoon Han, Sorim Kim, Yang Tae Jang, Jumyoung Jekal, Janghwan Kim, Jae Geun Jang, Kyung-In Xu, Sheng Gao, Wei Cho, Il-Joo Yu, Ki Jun Lee, Taeyoon https://scholar.dgist.ac.kr/handle/20.500.11750/60416 2026-06-15T00:40:20Z 2026-04-30T15:00:00Z

Title: Structurally engineered ultrasoft PEDOT:PSS fiber microelectrodes with enhanced electrochemical performance for neural interfaces Author(s): Won, Chihyeong; Cho, Young Uk; Kweon, Siyeon; Cho, Sungjoon; Kwon, Chaebeen; Kim, Hyun Woo; Lee, Ju Young; Park, Sang Hoon; Han, Sorim; Kim, Yang Tae; Jang, Jumyoung; Jekal, Janghwan; Kim, Jae Geun; Jang, Kyung-In; Xu, Sheng; Gao, Wei; Cho, Il-Joo; Yu, Ki Jun; Lee, Taeyoon Abstract: Stable and reliable neural interfacing is essential for the diagnosis and treatment of chronic neurological disorders. Flexible neural probes are particularly important for this purpose, as they minimize tissue damage and inflammatory responses while maintaining stable electrode-tissue coupling; however, achieving both high electrical performance and tissue-like mechanics remains challenging. Here, we present a poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) fiber microelectrode (PFME), an all-organic neural probe capable of recording single-neuron activities with potential for long-term interfacing. The PFME is entirely composed of organic components and fabricated without thermal processing. In addition, the posttreatment process enables to selectively remove PSS binder networks while promoting PEDOT chain alignment to optimize mechanical compliance and electrochemical performance. In vivo, the PFME enables stable single-unit recordings from the mouse hippocampus. Histological analysis after 1 week of implantation reveals minimal glial activation comparable to that elicited by a conventional probe. This structurally engineered PFME establishes a pathway to achieve minimally invasive neural interfacing platforms for chronic applications.

2026-04-30T15:00:00Z Park, Yae Won Kang, Myeongkyun Ryu, Huiseung Han, Kyunghwa Sim, Yongsik Park, Ji Eun Chang, Jong Hee Kim, Se Hoon Lee, Seung-Koo Park, Sang Hyun Ahn, Sung Soo https://scholar.dgist.ac.kr/handle/20.500.11750/60415 2026-06-12T05:10:10Z 2026-03-31T15:00:00Z

Title: A robust vision language model for molecular status prediction and radiology report generation in adult-type diffuse gliomas Author(s): Park, Yae Won; Kang, Myeongkyun; Ryu, Huiseung; Han, Kyunghwa; Sim, Yongsik; Park, Ji Eun; Chang, Jong Hee; Kim, Se Hoon; Lee, Seung-Koo; Park, Sang Hyun; Ahn, Sung Soo Abstract: We aimed to establish a robust vision-language model ("Glio-LLaMA-Vision") for molecular status prediction and radiology report generation (RRG) in adult-type diffuse gliomas. Multiparametric MRI data and paired radiology reports from 1001 patients with adult-type diffuse gliomas were included in the institutional training set. A vision-language model, Glio-LLaMA-Vision, was developed from LLaMA 3.1 pre-trained on 2.79 million biomedical image-text pairs from PubMed Central and further fine-tuned from the institutional training set. The performance was validated in 100 patients and 75 patients with paired MRI-radiology reports from an institutional validation set and another tertiary institution (AMC), and in 170 and 477 patients with MRI from TCGA and UCSF datasets, respectively. In terms of IDH mutation status prediction, Glio-LLaMA-Vision showed AUCs ranging from 0.85-0.95 in the internal validation and external datasets. In terms of RRG, the BLEU-1 and ROUGE-L scores were 0.50 and 0.49 in the internal validation, respectively, and 0.32 and 0.36 on the AMC dataset, respectively. Overall, 37.8% of generated reports were considered superior or equal to the original reports, while 91.0% of generated reports were considered clinically acceptable by neuroradiologists. In conclusion, Glio-LLaMA-Vision demonstrates promising performance in molecular status prediction and RRG in adult-type diffuse gliomas, showing potential for clinical assistance.

2026-03-31T15:00:00Z