研究課題/領域番号 |
22K14713
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研究種目 |
若手研究
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配分区分 | 基金 |
審査区分 |
小区分34030:グリーンサステイナブルケミストリーおよび環境化学関連
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研究機関 | 名古屋大学 |
研究代表者 |
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研究期間 (年度) |
2022-04-01 – 2025-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
4,550千円 (直接経費: 3,500千円、間接経費: 1,050千円)
2024年度: 780千円 (直接経費: 600千円、間接経費: 180千円)
2023年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2022年度: 2,340千円 (直接経費: 1,800千円、間接経費: 540千円)
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キーワード | natural resources / carbon materials / database / cellulose conversion / bio-derived carbon / conversion / cellulose / catalyst |
研究開始時の研究の概要 |
This study will apply ML to identify relationship of raw materials, process, properties, and performance and develop generic prediction models to find key factors for achieving high-performance catalysts for cellulose conversion, which will provide solid ideas for development to commercial scale.
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研究実績の概要 |
The research is divided into two parts. One is building database and material informatics. In this part, we have sourced 112 raw materials including both plant-based and animal-based materials. The characterization has been done by FTIR (50 raw materials), XRD (50 raw materials), and SEM (20 raw materials). 20 raw materials were converted into carbon materials. The selection of descriptors and data pre-processing methods have been decided based the demonstration of building data and predictions of 138 commercial carbons (20k data). Part two is the synthesis of catalyst and evaluation for conversion of cellulose. In this part, the preparation for evaluation of products from conversion of cellulose by using the synthesized catalysts have been started, such as setting up the reactor and HPLC.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The research can be proceeded faster than the recent situation, if the new electric furnace can be purchased. The HPLC column and standards have to be ordered from oversea. Therefore, it takes long time.
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今後の研究の推進方策 |
The characterization (FTIR, XRD, SEM) of both raw materials and carbon materials is planned to be completed by the end of this fiscal year, as well as the preparation evaluation of catalysts. The data will be collected, and the prediction models are planned to be demonstrated. After that, the increase of raw materials and more characterization (TEM, BET, etc.) are also planned to conduct to increase the quality of database.
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