Advancing our understanding of heterogeneous catalysis is critical for resolving the kinetic challenges in lithium-sulfur batteries (LSBs). Herein, we propose a theoretical framework: the dual d-band model, which extends the classical d-band center theory by introducing two distinct catalytic sites with complementary d-band centers. Specifically, by strategically integrating two distinct catalytic sites with complementary d-band centers, where one aligns with the lowest unoccupied molecular orbital (LUMO) of sulfur species to optimize the sulfur reduction reaction (SRR) and the other aligns with the highest occupied molecular orbital (HOMO) to accelerate the sulfur evolution reaction (SER), the redox kinetics of sulfur species is effectively balanced. To verify this hypothesis, we developed a dual-site catalyst, Mn-RuO₂ (MRO), featuring Ru sites tailored for SRR and the supplementary Mn sites optimized for SER. Leveraging this dual-site synergy, the MRO-based cell achieved superior performance under limited electrolyte conditions. This work presents a promising strategy to regulate sulfur redox reactions for high-performance LSBs.

Potassium oxide (K₂O) is used as a promotor in industrial ammonia synthesis, although metallic potassium (K) is better in theory. The reason K₂O is used is because metallic K, which volatilizes around 400 °C, separates from the catalyst in the harsh ammonia synthesis conditions of the Haber-Bosch process. To maximize the efficiency of ammonia synthesis, using metallic K with low temperature reaction below 400 °C is prerequisite. Here, we synthesize ammonia using metallic K and Fe as a catalyst via mechanochemical process near ambient conditions (45 °C, 1 bar). The final ammonia concentration reaches as high as 94.5 vol%, which was extraordinarily higher than that of the Haber-Bosch process (25.0 vol%, 450 °C, 200 bar) and our previous work (82.5 vol%, 45 °C, 1 bar).

Electroluminescence from quantum dots (QDs) is a suitable photon source for futuristic displays offering hyper-realistic images with free-form factors. Accordingly, a nondestructive and scalable process capable of rendering multicolored QD patterns on a scale of several micrometers needs to be established. Here, nondestructive direct photopatterning for heavy-metal-free QDs is reported using branched light-driven ligand crosslinkers (LiXers) containing multiple azide units. The branched LiXers effectively interlock QD films via photo-crosslinking native aliphatic QD surface ligands without compromising the intrinsic optoelectronic properties of QDs. Using branched LiXers with six sterically engineered azide units, RGB QD patterns are achieved on the micrometer scale. The photo-crosslinking process does not affect the photoluminescence and electroluminescence characteristics of QDs and extends the device lifetime. This nondestructive method can be readily adapted to industrial processes and make an immediate impact on display technologies, as it uses widely available photolithography facilities and high-quality heavy-metal-free QDs with aliphatic ligands.

Photopatterning In article number 2205504, BongSoo Kim, Wan Ki Bae, Moon Sung Kang, and co-workers show how light-emissive quantum dots can be directly photopatterned using additives containing multiple azide units that undergo a UV-induced crosslinking reaction with the surface ligands of quantum dots. Patterns of red, green, and blue quantum dot patterns can be successfully formed at micrometer-scale resolution using photolithography facilities widely available in industry.

Most robots today are naked, and nobody seems to mind! We argue they should. Robot attire is not cosmetic: clothing systematically shapes social cognition, cueing role expectations, warmth and competence inferences, touch norms, and trust. We introduce the Robot Fashion Psychology Model (RFPM), a mid-level theory linking what robots wear to how people interact with them, moderated by morphology and context. Grounded in fashion-led research-through-design that triangulates in-the-wild exemplars, a capsule collection spanning humanoids to drones, and reflexive critique - we translate RFPM into morphology aware garment guidelines and ethical guardrails against "trust-washing" and capability deception. We then ask: What happens when robots dress for each other? When fashion becomes visible only to machines? Robot wardrobes are coming. With them come new forms of designed influence. We offer foundations for designing them with intention rather than improvisation, while norms are still forming.

Lithium-ion batteries (LIBs) play a pivotal role in energy storage systems (ESSs) and electric vehicles (EVs); however, they inevitably undergo capacity fading and performance degradation during long-term operation. Accordingly, accurate state-of-health (SOH) estimation is essential for reliable battery management. In this study, refined health indicators (HIs) are defined from charge– discharge voltage–time characteristics, and a flexible SOH prediction framework is proposed. Aging data obtained from INR21700-33J cells over 1,400 cycles are analyzed to extract multiple HIs, including mean voltage falloff (MVF), voltage interval of equal discharging/charging time difference (VIEDTD/VIECTD), and time interval of discharging/charging voltage difference (TIEDVD/TIECVD). These indicators are further subdivided using voltage resolutions of 0.1 V and 0.01 V, enabling robust HI extraction under data imbalance. To compensate for missing HI data caused by external operational factors in practical ESS environments, a denoising autoencoder (DAE)-based interpolation method is employed to preserve temporal degradation trends. Subsequently, recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models are applied for SOH prediction. Among them, the GRU model achieves the best performance, with an MAE of 2.24, RMSE of 2.74, and R² of 0.97, demonstrating improved accuracy and robustness under incomplete operational data conditions.

Transportation electrification is a critical solution to reducing emissions and combating global climate change by shifting from fossil fuels to sustainable energy sources. However, the growing demand for fast charging introduces thermal challenges, increasing risks such as thermal runaway and battery degradation, especially in extreme temperatures. This study presents a machine-learning approach for predicting lithium-ion battery (LIB) temperature using electrochemical impedance spectroscopy (EIS) features with minimal sensitivity to the state-of-charge (SOC) and state-of-health (SOH). Through comprehensive analysis using the Pearson correlation coefficient (PCC), the study identifies EIS frequency ranges strongly correlated with temperature but remain unaffected by SOC or SOH variations. A support vector regression (SVR) model trained on these features achieves high accuracy, with RMSE and MAE values of 1.35°C and 0.81°C, respectively. The findings highlight the EIS magnitude at 6252 Hz as a reliable predictor of LIB temperature, offering significant advancements in thermal management for safer and more efficient battery operation.

This study, a model is proposed that generates descriptive sentences for input images visually impaired individuals. For this purpose, a novel image captioning approach is introduced, integrating the principles of human visual Understanding mechanisms with the Vision Transformer (ViT) architecture, further enhanced by deep reinforcement learning. First, features are extracted from the image based on human visual perception. Second, the image features are encoded through the encoding block of ViT and input into the long short-term memory (LSTM) network to generate annotations for the image. Finally, reinforcement learning is optimized to further enhance the accuracy of the generated captions. Evaluations were performed utilizing the MSR-VTT benchmark dataset, which is widely used for image captioning tasks. Experimental results on the MSR-VTT benchmark dataset demonstrate that the proposed model achieves BLEU-4 of 43.0, METEOR of 29.1, ROUGE-L of 62.7, and CIDEr-D of 54.9, surpassing state-of-the-art baseline models across all evaluation metrics. The proposed model can be applied to video annotation applications for the visually impaired. In contrast to prior works that primarily rely on conventional convolutional architectures, the proposed model uniquely incorporates human-inspired visual perception principles and Vision Transformer-based global encoding, offering a novel and interpretable framework tailored for assistive image captioning.

Advancing our understanding of heterogeneous catalysis is critical for resolving the kinetic challenges in lithium-sulfur batteries (LSBs). Herein, we propose a theoretical framework: the dual d-band model, which extends the classical d-band center theory by introducing two distinct catalytic sites with complementary d-band centers. Specifically, by strategically integrating two distinct catalytic sites with complementary d-band centers, where one aligns with the lowest unoccupied molecular orbital (LUMO) of sulfur species to optimize the sulfur reduction reaction (SRR) and the other aligns with the highest occupied molecular orbital (HOMO) to accelerate the sulfur evolution reaction (SER), the redox kinetics of sulfur species is effectively balanced. To verify this hypothesis, we developed a dual-site catalyst, Mn-RuO₂ (MRO), featuring Ru sites tailored for SRR and the supplementary Mn sites optimized for SER. Leveraging this dual-site synergy, the MRO-based cell achieved superior performance under limited electrolyte conditions. This work presents a promising strategy to regulate sulfur redox reactions for high-performance LSBs.

Potassium oxide (K₂O) is used as a promotor in industrial ammonia synthesis, although metallic potassium (K) is better in theory. The reason K₂O is used is because metallic K, which volatilizes around 400 °C, separates from the catalyst in the harsh ammonia synthesis conditions of the Haber-Bosch process. To maximize the efficiency of ammonia synthesis, using metallic K with low temperature reaction below 400 °C is prerequisite. Here, we synthesize ammonia using metallic K and Fe as a catalyst via mechanochemical process near ambient conditions (45 °C, 1 bar). The final ammonia concentration reaches as high as 94.5 vol%, which was extraordinarily higher than that of the Haber-Bosch process (25.0 vol%, 450 °C, 200 bar) and our previous work (82.5 vol%, 45 °C, 1 bar).

Electroluminescence from quantum dots (QDs) is a suitable photon source for futuristic displays offering hyper-realistic images with free-form factors. Accordingly, a nondestructive and scalable process capable of rendering multicolored QD patterns on a scale of several micrometers needs to be established. Here, nondestructive direct photopatterning for heavy-metal-free QDs is reported using branched light-driven ligand crosslinkers (LiXers) containing multiple azide units. The branched LiXers effectively interlock QD films via photo-crosslinking native aliphatic QD surface ligands without compromising the intrinsic optoelectronic properties of QDs. Using branched LiXers with six sterically engineered azide units, RGB QD patterns are achieved on the micrometer scale. The photo-crosslinking process does not affect the photoluminescence and electroluminescence characteristics of QDs and extends the device lifetime. This nondestructive method can be readily adapted to industrial processes and make an immediate impact on display technologies, as it uses widely available photolithography facilities and high-quality heavy-metal-free QDs with aliphatic ligands.