A5100456941- Catalysis for Biomass Conversion
- Carbon dioxide utilization in catalysis
- Catalysis and Hydrodesulfurization Studies
- Lignin and Wood Chemistry
- Heat shock proteins research
- Mechanical and Thermal Properties Analysis
- Multimodal Machine Learning Applications
- Intelligent Tutoring Systems and Adaptive Learning
- Plant Molecular Biology Research
- Biofuel production and bioconversion
- Catalytic Processes in Materials Science
- Topic Modeling
- Icing and De-icing Technologies
- Clay minerals and soil interactions
- Human Pose and Action Recognition
- Natural Language Processing Techniques
- Pasture and Agricultural Systems
- Educational Assessment and Pedagogy
- Plant Stress Responses and Tolerance
- Oxidative Organic Chemistry Reactions
- Soil Carbon and Nitrogen Dynamics
- Enzyme Catalysis and Immobilization
- Transportation Safety and Impact Analysis
- Advanced Algorithms and Applications
Lanzhou University
2022-2025
Ministry of Agriculture and Rural Affairs
2021-2025
Yeshiva University
2024
Institute of Grassland Research
2022-2024
Agro-Environmental Protection Institute
2021-2023
State Key Laboratory of Herbage Improvement and Grassland Agro-ecosystems
2022
Hebei Agricultural University
2015
Reaction pathways for conversion of agricultural waste biomass into formic acid are reviewed established (fast pyrolysis, hydrolysis, wet oxidation, catalytic oxidation), and cutting-edge (photocatalysis, electrocatalysis) methods.
Formic acid is a versatile chemical, which industrially produced from fossil resources. In this work, biomass-derived xylose was used as potential feedstock to synthesize formic with Mn–Ce oxides the catalyst in water. Among different molar ratios, Mn4Ce0.05Ox showed best catalytic activity, offering yield up 65.1% at 160 °C 3 MPa of O2, much higher than that using pristine MnOx (40.5%) and CeOx (9.9%). addition, shown be tolerant concentration. The introduction into increased total surface...
MnO x was used as a vanadium-free catalyst for conversion of glucose to formic acid in water with 81% yield which α-scission (C1–C2 bond cleavage) arabinose being an intermediate found be the major pathway.
Visual Question Answering (VQA) research seeks to create AI systems answer natural language questions in images, yet VQA methods often yield overly simplistic and short answers. This paper aims advance the field by introducing Explanation (VQE), which enhances ability of provide detailed explanations rather than brief responses address need for more complex interaction with visual content. We first created an MLVQE dataset from a 14-week streamed video machine learning course, including 885...