The development of stable Fe-based catalysts for CO2 hydrogenation to long-chain hydrocarbons faces a critical trade-off between selectivity and durability. Here, we demonstrate that ultra-high potassium loading (up to 30 wt %) on FeCu/gamma-Al2O3 catalysts modifying this limitation through electronic modification and mixed-phase stabilization. The optimized 25 wt% K catalyst achieves notable C5+ hydrocarbon selectivity of 52.6% while maintaining stable performance over 100 h. High-loading potassium promotion transforms the catalyst through: (1) electronic donation accelerating Fe5C2 formation, (2) enhanced surface basicity suppressing hydrogenation, and (3) stabilization of mixed Fe3O4/Fe5C2 phases resistant to deactivation. Although increased K induces carbon deposition, the deposited carbon is predominantly amorphous and carbide-type and is not associated with measurable activity loss within the test window. The gamma-Al2O3 support effectively disperses excess potassium, preserving catalyst porosity at ultra-high loadings. Long-term testing confirms sustained activity over 100 h with 15.6% C5+ yield and suppressed methane formation (12% selectivity), providing a design strategy for industrial CO2-to-fuel applications.
Social activities result from complex joint activity-travel decisions between group members. While observing the decision-making process of these activities is difficult via traditional travel surveys, the advent of new types of data, such as unstructured chat data, can help shed some light on these complex processes. However, interpreting these decision-making processes requires inferring both explicit and implicit factors. This typically involves the labor-intensive task of manually reading and annotating dialogues to capture context-dependent meanings shaped by social and cultural norms. Against this background, this study evaluates the potential of Large Language Models (LLMs) to automate and complement human annotation in interpreting decision-making processes from group chats, using data on joint eating-out activities in Japan as a case study. We designed a prompting framework inspired by the knowledge acquisition process in Knowledge Graph (KG) construction. This framework sequentially extracts key decision-making factors, including the group-level restaurant choice set and outcome, individual preferences of each alternative, and the specific attributes driving those preferences. This structured process guides the LLM to interpret group chat data step-by-step, converting unstructured dialogues into structured tabular data describing decision-making factors. To evaluate LLM-driven outputs, we conduct a quantitative analysis using a human-annotated ground truth dataset and a qualitative error analysis to examine model limitations. Results show that while the LLM reliably captures explicit decision-making factors, it struggles to identify nuanced implicit factors that human annotators readily identified. We further pinpoint specific contexts where the LLM performs poorly, establishing a boundary for when LLM-based extraction can be trusted versus when human oversight remains essential. These findings highlight both the potential and limitations of LLM-based analysis for incorporating non-traditional data sources on social activities into activity-based models.
Eliminating heavy metals from aqueous solutions continues to be of major environmental concern, primarily due to their non-biodegradable nature and hazardous effects. In this study, a Ca-modified low-temperature pine bark biochar (CaPB300) was developed as a sustainable and cost-effective adsorbent. The adsorption behavior of Cu(II) and Ni(II) in both single and binary systems was systematically investigated. Thermal treatment converted native carboxylic acid groups into deprotonated carboxylate (−COO−) groups, which acted as primary adsorption sites. The adsorption process was mainly governed by surface complexation, indicating a chemisorption-dominated mechanism. The Langmuir model demonstrated that in isolation, Cu(II) and Ni(II) demonstrated similar adsorption capacities at (56.85 and 55.88) mg/g, respectively. However, the introduction of a binary system significantly altered these figures to (65.20 and 11.45) mg/g for Cu(II) and Ni(II), respectively, confirming that when competing for available sites, Cu(II) outperforms Ni(II). In the binary system, Cu(II) was preferentially adsorbed over Ni(II) due to its stronger affinity toward carboxylate groups, leading to the formation of more stable surface complexes. Response surface methodology (RSM) was used to optimize the parameters for the adsorption process. The predicted removal efficiencies approached 100% for Cu(II) in both single and binary systems, while under optimized conditions, Ni(II) removal reached up to 99.39%. Overall, CaPB300 demonstrated efficient removal of Cu(II) and Ni(II), with pronounced selectivity toward Cu(II). These findings demonstrate the potential of CaPB300 as a cost-effective and sustainable adsorbent to treat multi-metal wastewater.
Fuels derived from hydrothermally treated sludge are promising waste-to-energy resources, but ash deposition and high-temperature corrosion limit their practical use. This study evaluates whether these risks follow the same trend. Three fuels with distinct ash chemistries were examined using combustion deposition tests, SUS 316 corrosion experiments, thermogravimetric/differential thermal analysis (TGA/DTA), and X-ray diffraction (XRD). SS-B had the highest ash content (54.5 wt%), SS-C the lowest (17.2 wt%), and SS-A an intermediate content (26.5 wt%). Deposit ratios for SS-A, SS-B, and SS-C were 0.278, 0.339, and 0.221, respectively, giving the order SS-B > SS-A > SS-C. By contrast, specific mass changes of SUS 316 after 24 h at 1000 ℃ were 22.17, 4.78, and 80.00 mg/cm2, respectively, giving the corrosion-severity order SS-C > SS-A > SS-B. Specific mass changes remained low at 600 and 800 ℃ but increased sharply at 1000 ℃, indicating that severe ash-induced corrosion occurred only at the highest temperature. The TGA/DTA residual-mass order was SS-B > SS-A > SS-C and therefore did not explain corrosion severity. XRD showed AlPO4, (Ca,Mg)3(PO4)2, and Fe2O3 in SS-A, while AlPO4-rich phases in SS-B were associated with lower corrosion. SS-C contained larger Fe2O3 and (Ca,Mg)3(PO4)2 fractions and formed Fe2O3, FeO, Ca2Fe2O5, and AlPO4 on SUS 316. Thus, corrosion risk should be assessed separately from deposition tendency, with attention to Fe-rich ash chemistry, chlorine, phosphate stabilization, and ash–metal reaction products.
Construction sites pose a high risk of struck-by accidents due to limited situational awareness. This study investigates the feasibility of using a neckband-style wearable 360 degrees camera for proximity warning in construction environments. The proposed framework consists of a data preprocessing module-handling motion blur detection and distortion correction-and a hazard detection module, which integrates object detection with depth estimation. By identifying hazardous equipment and estimating its distance, the system provides personalized warnings to workers at risk. Field tests conducted at actual construction sites demonstrated an F1-score of 0.81 in hazard detection. Unlike fixed cameras or sensor-based systems, this approach enables omnidirectional, individualized hazard monitoring without additional site infrastructure. The wearable camera also minimizes interference with tasks, supporting stable image capture. The findings highlight the potential of combining wearable computer vision and deep learning to improve safety in complex and dynamic construction settings.
