Modular Photo-Battery Adapts to Sunlight and Indoor Light

Abstract The adoption of autonomous smart devices necessitates new solutions for energy harvesting and storage to power them. Photo-rechargeable batteries, which integrate these functions into a single device, have gained traction as compact and cost-efficient solution for such applications. However, the implications of different system designs and material choices on photo-battery performance remain poorly understood. Here, we first compare the battery performance of solid Li-ion battery cathode materials (eg LiFePO4 and LiNi0.8Co0.1Mn0.1O2) with liquid catholytes (eg polyiodide and Cu2+/+(dmby)2) for photo-battery applications. The results indicate that the closely packed nature of solid-phase active materials allow for higher volumetric energy density and rate performance. Building on these findings, we present a device architecture for an integrated photo-rechargeable lithium metal battery incorporating a LiFePO4 cathode and a dye-sensitized PV-electrode. These compact photo-batteries can self-charge to 70% state of charge within 10 min under 1-sun illumination and also operate under indoor light. Through simultaneous photo-charging and discharging, we demonstrate that the battery output can be significantly enhanced solely by light energy, offering a clear strategy for future photo-battery design. Photo-rechargeable batteries combine light harvesting and energy storage in a single device, making them well suited to autonomous devices that cannot easily be connected to a power source or have their batteries replaced. But their performance depends on how the photovoltaic and battery components are designed to work together. A team, led by Professor Tae-Hyuk Kwon of the Department of Chemistry at UNIST and Professor Michael De Volder of the University of Cambridge has developed a modular photo-rechargeable lithium-metal battery that can be configured for different lighting conditions, including sunlight and indoor light. Rather than starting with a single battery design, the researchers first compared solid lithium-ion cathode materials with liquid catholytes to determine how each affects photo-battery performance. Solid cathodes, including lithium iron phosphate (LFP), delivered higher volumetric energy density and better rate performance, an advantage the researchers attribute to the more closely packed nature of solid active materials. Based on these findings, the team built an integrated photo-battery combining an LFP cathode, a lithium-metal anode, and a dye-sensitized photovoltaic electrode. The modular design allows the photovoltaic units to be arranged to provide the voltage required to charge the battery, while the battery component itself remains unchanged. Under 1-sun illumination, the device reached 70% state of charge within 10 minutes and delivered a discharge energy density of 327.8 mWh g⁻¹. A configuration designed for lower-light conditions also charged and discharged using 1,000-lux indoor light, roughly the illumination level of a well-lit office. The team also tested what happens when the battery receives light while supplying power. Under simultaneous photo-charging and discharging, the device delivered greater output than it did in the dark, showing that incoming light can contribute additional energy during use. The modular structure makes it possible to adjust the charging voltage by changing how the photovoltaic units are connected. In the system demonstrated in the study, the researchers combined the battery unit with four additional photovoltaic units, allowing configurations of one, three, or five units. All five were used when the higher voltage required by the lithium-metal battery was needed. “By changing the number and type of photovoltaic units, we can match the charging voltage required by different battery materials,” said Dr. Byungman Kim of the Department of Chemistry at UNIST, first author of the study. “This modular approach could provide a useful design strategy for photo-rechargeable batteries using a wider range of battery chemistries.” Professor Kwon added, “The ability to charge under both outdoor and indoor light could make photo-rechargeable batteries useful for IoT devices where wired power or frequent battery replacement is impractical.” The findings were published in the August 2026 issue of Energy Storage Materials . The research was supported by Korea Electric Power Corporation (KEPCO) and UK Research and Innovation (UKRI). Journal Reference Byung-Man Kim, Arvind Pujari, Sungtae Kim, et al. , “Design strategy for integrated photo-rechargeable batteries with high energy density,” Energy Storage Mater., (2026).

2026.09.17

Satellite Method Improves Wildfire Burn-Severity Assessment Across Seasons

Abstract Change detection-based approaches that use satellite-derived spectral indices (SIs) and their pre- to post-fire differences (dSIs) to quantify burn severity have emerged as powerful alternatives to traditional field-based methods such as the Composite Burn Index (CBI). However, most studies have focused on improving model performance using ideal datasets—those with clearly defined burn areas and cloud-free pre- and post-fire imagery—while overlooking a key limitation: the requirement for phenological consistency between pre- and post-fire imagery. To address this gap, this study presents an Advanced burn Severity Analysis framework using Phenology-detrended indices (ASAP), which produces a refined burn severity index dataset that is robust to seasonal and phenological variability. The ASAP integrates automated delineation of analysis areas based on active fire detections, preparation of pre- and post-fire SI images suitable for vegetation analysis, and a phenology-detrending strategy to align seasonal vegetation conditions between image pairs. The key innovation of the framework is a novel phenology-detrended dSI (pdSI), which uses surrounding unburned areas to seasonally adjust the pre-fire baseline prior to calculating the pre- to post-fire difference, thereby enhancing wildfire-induced signals. Validation using CBI plots and Monitoring Trends in Burn Severity perimeters demonstrates that pdSI consistently matches or outperforms conventional dSIs. These improvements are especially pronounced when pre- and post-fire images are phenologically inconsistent, when phenology-sensitive indices such as differenced normalized difference vegetation index are applied, and in vegetation or climate types with strong seasonal variability, such as deciduous forests and herbaceous areas in temperate climates. Time-series analyzes further show that pdSI mitigates seasonal variability effects, delivering more stable and robust performance compared to conventional dSIs. The proposed framework was also successfully applied to recent large-scale wildfires in the Los Angeles area in 2025, demonstrating its strong applicability and reliability in operational contexts within the wildland–urban interface. Overall, this study provides a simple yet powerful solution to a long-standing challenge in change detection-based burn severity analysis. Satellite imagery is widely used to assess wildfire burn severity, but seasonal changes in vegetation can distort comparisons between pre- and post-fire images. A research team, led by Professor Jungho Im of the Department of Civil, Urban, Earth, and Environmental Engineering at UNIST has developed a framework that corrects for these seasonal differences, allowing a wider range of satellite imagery to be used for post-fire assessment. The framework, called ASAP (Advanced burn Severity Analysis with Phenology-detrended indices), uses nearby unburned vegetation to estimate how seasonal conditions have changed between the pre- and post-fire images. It then adjusts the pre-fire baseline before measuring the change caused by the wildfire. This matters because the spectral indices used to assess vegetation respond to both fire damage and normal seasonal change. If a summer image is compared directly with one taken in autumn, for example, leaf loss or declining vegetation activity may appear as part of the burn signal. ASAP uses comparable unburned areas to account for that background change rather than requiring the two images to be closely matched by season. The resulting phenology-detrended index (pdSI) consistently matched or outperformed conventional difference indices in comparisons with field-based burn-severity measurements. The gains were most apparent when pre- and post-fire images reflected differed seasonal conditions, particularly in vegetation with strong seasonal cycles, such as deciduous forests and herbaceous areas. Across 12 US wildfires, ASAP maintained a correlation of 0.72 with field observations using seasonally mismatched imagery, nearly identical to the 0.73 achieved with imagery from similar seasonal conditions. The framework also automates key parts of the analysis, using active-fire detections to define the area to be assessed and screening imagery for clouds and areas unsuitable for vegetation analysis. This reduces the manual preparation needed before burn severity can be mapped. When applied to five wildfires in the Los Angeles area in January 2025, ASAP reduced the misclassification of unburned areas caused by seasonal vegetation differences and more clearly identified the extent of fire-affected areas. “Conventional satellite-based assessments often depend on finding pre- and post-fire images taken under similar seasonal conditions,” said Professor Im. “By accounting for seasonal vegetation change, we can broaden the range of usable imagery and move toward more consistent, automated burn-severity assessment.” The approach could support faster assessment after large wildfires and provide more timely information for recovery planning and satellite-based disaster monitoring. Taejun Sung and Seyoung Yang served as co-first authors of the study. The research was supported by the Korea Forest Service and the Ministry of Education. The findings were published in the September issue of Remote Sensing of Environment. Journal Reference Taejun Sung, Seyoung Yang, Woohyeok Kim, et al. , “Enhancing satellite-based wildfire monitoring: An advanced framework for burn severity analysis with phenology-detrended indices,” Remote Sens. Environ., (2026).

2026.09.14

AI Personalization Adapts Answers to What Users Know

Abstract Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. We present ExPerT, a query-wise personalization framework that adapts LLM responses to users' query domain expertise by combining semantic and behavioral cues. ExPerT consists of two key components: (i) a semantic-behavioral expertise inference module that jointly interprets query text and keystroke dynamics via in-context LLM prompting, and (ii) an expertise-conditioned response generation that adapts the level of detail, terminology, and conceptual complexity. Our user study with 40 participants and 1270 queries demonstrated that ExPerT reduced expertise inference error by 65.7% compared to the strongest baseline (MAE = 0.398 vs. 1.162) and improved response satisfaction by 17.52% (from 3.71 to 4.36) on a 5-point Likert scale. A person's expertise is rarely the same across every subject. Someone deeply familiar with one field may know little about another, and even within the same field, expertise can vary from topic to topic. Yet, AI systems often personalize responses using relatively fixed information, such as user profiles or previous conversations. Led by Professor Taesik Gong of the Department of Computer Science and Engineering at UNIST, researchers developed ExPerT, an AI personalization framework that adapts large language model (LLM) responses to a user's expertise for each question. It combines what a user asks with patterns in how they type, then adjusts the detail, terminology, and complexity of the response. Existing personalization methods can capture general preferences or background but may miss how much someone knows about a particular topic. ExPerT instead assesses expertise for each query, drawing on both the wording of the question and typing patterns such as keystroke timing, speed, and corrections. These signals are used to estimate expertise on a five-level scale, from novice to expert. Typing behavior provided useful clues. Participants with greater expertise tended to type domain-specific terms faster and with more consistent timing. But neither language nor typing behavior was reliable on its own: an expert may ask a simple question, while someone less familiar with a subject may use technical terms fluently. Combining the two helped ExPerT distinguish between such cases. The team evaluated ExPerT with 40 participants across chemistry, computer science, and business, collecting 1,270 queries along with typing data and self-reported expertise. Adding typing behavior to query semantics reduced the average expertise-estimation error from 0.488 to 0.398 levels, an improvement of 18.4%. Compared with the strongest existing baseline, ExPerT reduced the error by 65.7%, from a mean absolute error of 1.162 to 0.398. Expertise-adapted responses also received higher satisfaction ratings, rising from 3.71 to 4.36 on a five-point scale, an increase of 17.52%. “Existing personalization methods often rely on fixed profiles or previous conversations, making it difficult to reflect how much a user knows about the topic at hand,” said Professor Gong. “By adapting to expertise at the level of each question, ExPerT could support AI systems that communicate more effectively with users of different backgrounds and levels of knowledge.” The approach could be particularly useful in areas where expertise varies widely among users, including education, professional customer support, and specialized services in medicine, law, and finance. Yeji Park, a graduate student in UNIST's Graduate School of Artificial Intelligence and first author of the study, presented the work at ACL 2026 in San Diego. The paper was selected for an oral presentation, placing it among the top 4% of submissions, and received a SAC Highlight Award , given to the top 2%. The dataset and source code are publicly available through theExPerT GitHub repository. The research was supported by the Ministry of Science and ICT (MSIT), the Institute of Information & Communications Technology Planning & Evaluation (IITP), and the National Research Foundation of Korea (NRF), through programs supporting AI research and talent development. Journal Reference Yeji Park, Jiwon Tark, and Taesik Gong, "ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues," ACL '26, (2026).

2026.09.09

Perovskite Solar Cells Hold Up Under Extreme Temperature Swings

Summary The advent of self-assembled monolayer (SAM)-based hole-selective layers (HSLs) has revolutionized inverted perovskite solar cells (i-PSCs) by enabling record efficiencies, but they are constrained by incomplete surface coverage and interfacial heterogeneity. We devise topology-governed SAMs featuring isomeric linker patterns (LY-ortho [o], LY-meta [m], and LY-para [p]) and uncover the principle that conformational ordering dictates surface coverage and interfacial interaction coupling with perovskite. The topology-optimized LY-m forms a homogeneously dense, stable HSL with superior energy alignment and interfacial passivation. Devices incorporating LY-m achieve a record-high open-circuit voltage of 1.231 V, accompanied by an efficiency of 26.39% (certified 26.37%), while maintaining robust operational stability and scalability. Beyond laboratory metrics, LY-m-based devices withstand −100°C to 100°C thermal cycling with only ∼9% loss, far exceeding the operational limits of state-of-the-art commercial SAMs, while maintaining over 80% of their initial performance at 85°C (T80 ≅ 2,000 h). This work resolves the long-standing role of linker topology and paves the way for extreme-environment-resilient and scalable perovskite photovoltaics. Temperature swings are a major challenge for perovskite solar cells (PSCs), particularly in environments such as space, where materials may repeatedly move between extreme cold and heat. Led by Professors Changduk Yang and Seungjae Shin of the Department of Energy and Chemical Engineering and Professor Myung Hoon Song of the Department of Materials Science and Engineering at UNIST, the team developed perovskite solar cells that withstand such extremes by stabilizing a critical molecular interface. The cells retained about 91% of their initial efficiency after cycling between −100°C and +100°C, with a certified power-conversion efficiency of 26.37%. The key to this advancement is a self-assembled monolayer (SAM), a molecule-thin layer that helps move electrical charges between the electrode and the perovskite absorber. Under severe thermal stress, SAM molecules can shift or cluster, disrupting the interface and degrading performance. The team addressed this problem by changing where the molecular components of the SAM connect. They synthesized three versions with different linker positions and found that LY-m adopted a tilted orientation, allowing its molecules to pack densely and uniformly on the electrode. This arrangement also suppresses the molecular reorganization that can occur under large temperature swings. In thermal-cycling tests, LY-m cells were held alternately at −100°C and +100°C for 30 minutes at each temperature over 16 cycles. They remained operational with only about a 9% loss in efficiency, while cells using a commercial SAM failed under the same conditions. LY-m cells also reached a peak efficiency of 26.39%, with a certified efficiency of 26.37% and an open-circuit voltage of 1.231 V. At 85°C, they maintained more than 80% of their initial performance for approximately 2,000 hours. The approach also performed well at larger scales: a 1 cm² cell achieved 23.25% efficiency, while a 3 × 1 cm² mini-module reached 21.20%. “By changing only the position of the molecular connection, we were able to control how the molecules pack on the electrode,” said Professor Yang. “The resulting interface combines high efficiency with thermal stability, offering a promising route toward perovskite photovoltaics for environments with extreme temperature variations.” The study has been participated by Seunglok Lee and Jeewon Park of the Department of Energy and Chemical Engineering and Jingyu Yang of the Department of Materials Science and Engineering at UNIST, as co-first authors of the study. Published in Joule on August 3, the study has been supported by the Nano & Material Technology Development Program through the National Research Foundation (NRF) of Korea and the InnoCORE program of the Ministry of Science and ICT (MSIT). Journal Reference Seunglok Lee, Jinkyu Yang, Jeewon Park, et al., "Topology-governed self-assembled monolayers enable perovskite solar cells stable at −100°C to +100°C," Joule , (2026).

2026.09.08

AI Model Maps Seismic Responses Across Nuclear Power Plants

A research team, led by Professor Young-Joo Lee of the Department of Civil, Urban, and Environmental Engineering at UNIST and Senior Research Scientist Jaebeom Lee of the Korea Research Institute of Standards and Science (KRISS) has developed a deep-learning model for post-earthquake assessment of nuclear power plants. The model predicts seismic responses across the structure and estimates which locations may require priority inspection. Post-earthquake assessment can be challenging when sensors are installed at only a limited number of locations. Expanding sensor coverage throughout a nuclear power plant is difficult because of cabling, regulatory, and maintenance constraints. Numerical simulations can fill these gaps, but repeated finite element analyzes become computationally expensive when uncertainties in structural properties are taken into account. The researchers developed a deep-learning surrogate model that combines seismic ground motion with structural parameters to predict acceleration responses at 139 locations. This allows responses to be estimated at locations without physical sensors while reducing the computational cost of conventional analysis. The model also accounts for uncertainty in structural properties. Rather than providing only a binary assessment, it calculates the probability that the response at each location will exceed a predefined equipment-level threshold. These probabilities can help engineers prioritize locations for further inspection. Experimental validation showed an average maximum mean absolute percentage error of 1.37%, with R² values above 0.98. The model generates full acceleration time histories across multiple locations, allowing probabilistic responses to be assessed without repeatedly running computationally intensive finite element simulations. The team also developed a method for configuring the AI architecture according to a structure's natural frequencies. The resulting model achieved accuracy comparable to a deep-learning model with more than 200 times as many parameters, while requiring substantially fewer computational resources. The researchers expect the approach could also be adapted to other facilities that require rapid post-earthquake assessment, including semiconductor plants, data centers, and industrial facilities. “For safety applications, accurate prediction alone is not enough; it is also important to understand the uncertainty in those predictions,” the researchers said. “By quantifying that uncertainty, the framework can provide additional information for experts deciding where further inspection is needed.” The research was conducted jointly by the teams of Senior Research Scientist Jaebeom Lee at KRISS, with Jingoo Lee as first author. The work was supported by the National Research Foundation of Korea and KRISS. The findings have been reported in three papers, including a study published in Reliability Engineering & System Safety. The study was conducted jointly by the UNIST and KRISS research teams, with Jingoo Lee as first author. The work was supported by the National Research Foundation of Korea (NRF) and KRISS. The findings have been reported in three papers, including a study published in Reliability Engineering & System Safety (IF: 13.7). Journal Reference Jingoo Lee, Seungjun Lee, Young-Joo Lee, and Jaebeom Lee, “Predicting seismic floor response for nuclear power plant structures with time-series uncertainty propagation using attention-enhanced multimodal deep learning,” Reliab. Eng. Syst. Saf., (2026).

2026.09.07

Reducing Charge Loss in Organic Photoanodes for Solar Water Splitting

Abstract Organic photoelectrochemical (PEC) cells for solar water splitting typically utilize bulk heterojunction (BHJ) structures to circumvent the intrinsically short exciton diffusion lengths of organic semiconductors. However, stochastic donor/acceptor networks in BHJs often result in disordered interfacial contacts, leading to severe charge recombination. Herein, we report a pseudo-bilayer (PS-Bi) organic photoanode featuring a vertically ordered donor–acceptor configuration established via sequential deposition. Our systematic investigation reveals that this PS-Bi structure promotes selective charge transport and suppresses recombination due to its high domain purity, thereby facilitating efficient charge transfer at the interface. Consequently, the optimized PS-Bi photoanodes deliver an enhanced average photocurrent density (Jph) of 1.75 ± 0.03 mA cm−2 at 1.23 V versus the reversible hydrogen electrode for solar water oxidation, while simultaneously exhibiting a significant cathodic onset shift and extended operational stability compared to their BHJ counterparts (Jph = 1.44 ± 0.07 mA cm−2). This study demonstrates that employing the PS-Bi structure is a promising strategy for achieving high-performance and stable organic PEC systems for sustainable solar fuel production. Producing hydrogen from sunlight depends in part on how efficiently a photoelectrode can separate and move electrical charges. In organic photoelectrodes, however, the conventional practice of mixing semiconductor materials can create disordered pathways that allow those charges to recombine, reducing both performance and stability. A research team, led by Professors Han Hee Cho, Moon Kee Choi, and Myung Hoon Song of the Department of Materials Science and Engineering at UNIST, working with researchers at École Polytechnique Fédérale de Lausanne (EPFL), has developed an organic photoanode with a more ordered internal structure. The new design improved photocurrent by about 22% while extending operational stability. The team created what is known as a pseudo-bilayer (PS-Bi) structure by depositing two organic semiconductor materials in sequence. As the second layer is applied, it partially penetrates the first, forming a mixed region at the interface while preserving more distinct layers above and below. Conventional organic photoelectrodes typically use a bulk heterojunction (BHJ), in which donor and acceptor materials are mixed throughout the film. This arrangement helps generate and separate charges, but its irregular internal pathways can also bring electrons and holes back together before they contribute to the chemical reaction. The PS-Bi combines the advantages of both approaches. Charges are generated and separated in the mixed region, while the more ordered layers guide electrons and holes in different directions. This reduces recombination and allows more of the generated charge to drive water oxidation. In tests, the PS-Bi photoanodes achieved an average photocurrent density of 1.75 mA cm⁻², compared with 1.44 mA cm⁻² for conventional BHJ photoanodes, an increase of about 22%. The voltage required to initiate water oxidation also fell from 0.35 V to 0.15 V, allowing the reaction to begin with less external electrical input. The structural change also improved durability. Based on the time required for photocurrent density to fall to 0.5 mA cm⁻², the pseudo-bilayer photoanodes remained operational substantially longer than their BHJ counterparts. The researchers also found that the two structures degrade differently. In BHJ photoanodes, highly reactive holes can damage the organic semiconductor within the intermixed structure. The more ordered pseudo-bilayer reduces damage within the donor–acceptor region, helping the photoanode maintain its performance for longer. “Conventional organic photoelectrodes mix donor and acceptor materials throughout the film, which can lead to charge losses and faster degradation,” said Professor Cho. “By controlling how the two materials are arranged, we were able to improve both charge transport and operational stability.” He further added, “The lower operating voltage could also help in the development of systems that ultimately produce hydrogen using sunlight without an external electrical bias.” The study was published online in Chemical Engineering Journal on July 16, 2026. The research was supported by the National Research Foundation of Korea (NRF), funded by the Ministry of Science and ICT (MSIT), and the InnoCORE program. Journal Reference Gyu Won Chae, Seong Rae Kang, Jin Su Park, et al. , “A pseudo-bilayer organic photoanode for solar water oxidation,” Chem. Eng. J., (2026).

2026.09.03