HEИндивидуална стипендия2022–2024

DESCRIPTOR · Advanced simulations in electrocatalysis for efficient production of C3+ by carbon dioxide reduction

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2022-05-01 → 2024-04-30
Финансиране от ЕС: 175 609 €
Участници: 1
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Методи за симулация и машинно обучение се използват за разработване на катализатори, които превръщат въглеродния диоксид в горива и химикали като пропанола. Това помага за намаляване на парниковите газове в атмосферата и борбата с глобалното затопляне.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Advanced simulations in electrocatalysis for efficient production of C3+ by carbon dioxide reduction

The increasing atmospheric CO2 levels from fossil fuel use have led to global warming. The IPCC report warns that global warming will exceed 1.5°C in the 21st century if greenhouse gas emissions, mainly CO2, are not drastically reduced. To combat this, Europe aims to achieve net-zero emissions by 2050 through the European Green Deal. A critical step is converting CO2 and renewable electricity into fuels and chemicals. One major challenge of this process is the lack of an electrocatalyst that can reduce CO2 to high-energy, high-value long-chain hydrocarbons, like propanol. This project aims to use Machine Learning and simulations to help developing the effective CO2 electroreduction catalysts. In this reaction, oxide-derived copper catalysts have attracted widespread attention due to their excellent ability to promote carbon-carbon coupling. The outstanding performance of oxide derived Cu is typically attributed to their unique surface structures. However, the intense dynamic behaviour of such catalysts under reaction conditions leads to surface restructuring. Due to the catalyst's active nature, it is highly susceptible to oxidation during characterization processes using ex-situ experimental techniques, making it difficult to reflect its true structure during the reaction. Consequently, there remains significant controversy regarding the active sites, particularly the existence forms and distributions of oxygen atoms in the material under reaction conditions. Furthermore, traditional simulation methods such as first-principles calculations and classical molecular dynamics simulations struggle to balance accuracy and speed, thus failing to capture this complex dynamic process. Limited understanding of this fundamental process hinders further optimization of catalysts and reaction conditions. To address this challenge, the objective of the project is to provide atomic-level insights with the help of advanced machine learning techniques, thus providing design principles for better catalyst development.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

The climate change has raised concerns about closing the carbon cycle by converting CO2 and renewable electricity to chemically stored energy in the form of fuels and commodity chemicals. Among these, long-chain hydrocarbons and alcohols are more attractive because of their high energy density and value. Recently, reconstructed oxide-derived Cu (OD-Cu) catalysts have shown the potential to produce multicarbon species at lower overpotentials. However, few simulations have addressed the formation mechanism of these compounds due to the complex dynamics of this system under high currents, and the exact site for the excellent OD-Cu catalytic performance remains to be discovered. The DESCRIPTOR project aims to obtain the first generation of CO2 electroreduction catalyst with useful faradaic efficiencies towards C3+ products by employing computational simulations based on Density Functional Theory (DFT) and augmented by Machine Learning techniques. Firstly, suitable structures for the OD-Cu materials will be obtained through large scale Molecular Dynamics simulations based on Machine Learning potentials, by screening the most common ensembles identified via graph theory. Secondly, the mechanism towards C3+ will be identified via jDFTx scheme, and descriptors of activity and selectivity will be found through dimensionality reduction techniques. Finally, to assess and compare to experimental work from our collaborators, the contribution of the solvent/electrolyte and effect of experimental parameters will be investigated via ab initio Molecular Dynamics and microkinetic modelling. The structures will be characterized via simulations of X-ray Photoelectron Spectroscopy, and Raman spectra, etc. In summary, the outcome of DESCRIPTOR will have a direct scientific and social impact, by increasing the basic knowledge on catalysis of achieving renewable fuel sources and improving EUs industrial competitiveness within new technologies for CO2 reduction.

Оригинален текст от CORDIS (на английски).

Участници

FUNDACIO INSTITUT CATALA D'INVESTIGACIO QUIMICA · TARRAGONAКоординаторИспания