To accelerate the transition to a renewable energy society by discovering new materials, chemicals, and processes through multi-scale simulation and data science.
We interface multi-scale materials simulation and data science. Specifically, we develop innovative methods that accelerate materials design.
Electrochemical dopamine (DA) detection has been extensively studied for the practical diagnosis of neurological disorders. A major challenge in this system is to synthesize selective and sensitive DA sensing electrocatalysts in extracellular fluids, because critical interferents such as uric acid (UA) and ascorbic acid (AA) exhibit oxidation potentials similar to those of DA. Herein, we report an extremely selective and sensitive electrocatalyst for DA sensing prepared by vanadium selenide (V2Se9). A solution-based process for the first time was introduced to synthesize the V2Se9, showing unique DA-philic characteristic caused by exposure negative charge of crystal selenide. Owing to its distinctive features, the prepared V2Se9 electrode detected only DA in the presence of concentrated interferents. Furthermore, nano-structured V2Se9 electrode extremely improves DA sensing ability as low as practical detection limit with maintaining inactive interferent characteristic. More interestingly, an identical unique DA-sensing ability was also observed in a V2Se9 analogue—Nb2Se9. We believe that this finding provides new insights into the effect of the analyte-philic properties of electrode materials on the electrocatalytic response for selective analyte quantification.
The solar-driven catalytic reduction of CO 2 to value-added chemicals is under intensive investigation. The reaction pathway via *OCHO intermediate (involving CO 2 adsorbed through O-binding) usually leads to the two-electron transfer product of HCOOH. Herein, a single-atom catalyst with dual-atom-sites featuring neighboring Sn(II) and Cu(I) centers embedded in C 3 N 4 framework is developed and characterized, which markedly promotes the production of HCHO via four-electron transfer through the *OCHO pathway. The optimized catalyst achieves a high HCHO productivity of 259.1 μmol g −1 and a selectivity of 61% after 24 h irradiation, which is ascribed to the synergic role of the neighboring Sn(II)–Cu(I) dual-atom sites that stabilize the target intermediates for HCHO production. Moreover, adsorbed *HCHO intermediate is detected by in-situ Fourier transform infrared (FTIR) spectroscopy (C=O stretches at 1637 cm −1 ). This work provides a unique example that controls the selectivity of the multi-electron transfer mechanisms of CO 2 photoconversion using heteronuclear dual-atom-site catalyst to generate an uncommon product (HCHO) of CO 2 reduction.
Synthesizing new inorganic functional materials is a practical goal of materials science. While the advances in computational techniques accelerated the virtual design, the actual synthesis of predicted candidate materials still remain as an expensive and slow process. While a few initial studies attempted to predict the synthesis routes for inorganic crystals, the existing models do not yield the uncertainty of the predictions and could produce thermodynamically unrealistic precursor chemicals. Here, we propose an element-wise graph neural network to predict the inorganic synthesis recipes. The trained model outperforms the popularity-based statistical baseline model for top-k exact match accuracy test, showing the validity of our approach for inorganic solid-state synthesis. We further validate our model by the publication-year-split test, where the model trained based on the materials data until the year 2016 is shown to successfully predict the synthetic precursors for the materials synthesized after 2016. The high correlation between the classification score and prediction accuracy suggests that the prediction score can be interpreted as a measure of uncertainty.