| Project/Area Number |
23K04890
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 36010:Inorganic compounds and inorganic materials chemistry-related
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| Research Institution | Hiroshima University |
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Principal Investigator |
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| Project Period (FY) |
2023-04-01 – 2026-03-31
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| Project Status |
Granted (Fiscal Year 2023)
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| Budget Amount *help |
¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2025: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2024: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
Fiscal Year 2023: ¥3,640,000 (Direct Cost: ¥2,800,000、Indirect Cost: ¥840,000)
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| Keywords | ESDA / Density of states / DFT method / Machine learning / Support effect / Heterogeneous catalysis / DOS decomposition / Universal Descriptor / Heterogenous Catalysts / DFT calculations |
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| Outline of Research at the Start |
Without a detailed understanding of the electronic states of the catalysts’ active sites and the orbitals of the adsorbates involved in the interaction/reaction, it is unlikely to improve the existing catalysts, explore new possible catalytic materials, and design superior catalysts. Thus, this proposal aims to develop and apply my new concept, the electronic structure decomposition approach (ESDA), to explains the nature of the chemical bond between an adsorbate and a catalyst and that can be used to explore the materials' space to discover new and more functionalized catalysts.
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| Outline of Annual Research Achievements |
Over the last year, my research has focused on introducing and promoting the efficacy of the Electronic Structure Decomposition Approach (ESDA).
-Conference Presentations: ESDA has been promoted via presentations at local and international conferences, where it was praised as ingenious by peers.
-Publications: I published a paper: “Predicting CO Interaction and Activation on Inhomogeneous Ru Nanoparticles Using Density Functional Theory Calculations and Machine Learning Models” in the Journal of Physical Chemistry C, a highly reputable journal.
Additionally, I have finished writing a new paper, soon to be submitted to ACS Nano, exploring ESDA's capabilities and applications.
-Research Impact: ESDA is being tested for various systems involving different catalyst materials and adsorbates.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
A lot of effort and time are being dedicated to making this research great.
Several peers have accepted the ESDA and have called the approach "genius."
I am currently writing a second paper to be submitted to a high-impact journal.
Lastly, the ESDA is being expanded to confirm its universality for different catalytic systems by changing the catalyst and the adsorbate.
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| Strategy for Future Research Activity |
The methodology of this research is being expanded and applied to various systems consisting of different catalysts and adsorbates.
We are focusing on the universal application of ESDA.
Challenges encountered include optimizing the methodology for diverse systems and integrating more complex machine learning algorithms.
To address these, we will collaborate with experts in related fields and acquire updated computational resources to enhance our analysis capabilities.
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