Computational design of two-dimensional materials for energy conversion
Bibliographic record
Abstract
The discovery of graphene in 2004 has ignited a surge of interest in various twodimensional (2D) materials, such as transition metal dichalcogenides (TMDCs), 2D group IIInitrides (2D III-nitrides), and black phosphorus (BP).Thanks to their unique structures, 2D materials exhibit distinct properties including high conductivity, large specific surface area and high catalytic activity, being ideal candidates for a variety of applications.Recently, 2D materials have demonstrated great promise in energy-related applications.Particularly, 2D materials like 2DTMDCs have been extensively studied as effective electrocatalysts and photocatalysts for hydrogen evolution reaction (HER).However, many 2D materials show limited HER performance due to the low density of catalytic sites in their structures.To overcome this limitation, various strategies have been developed to engineer the 2D material structures so as to improve HER performance.Among different engineering strategies, phase boundary and alloying can provide attractive options as they are able to enhance the density of active sites while retaining the structural integrity of 2D materials.However, systematic research on engineering 2D materials via phase boundaries and alloying for catalytic applications remains rather limited, with the mechanisms underlying enhanced catalytic performance elusive.Such lack of mechanistic understanding is a critical obstacle hindering rational design of the properties of 2D materials in a predictable manner.In this regard, the present thesis systematically studied the two important engineering strategies of 2D materials, i.e., via phase boundaries and alloying, and their roles in improving the HER performance.The focus is placed mainly on 2D TMDCs as the representative 2D material group, but with other 2D materials, i.e., 2D III-nitride alloys, also considered for generality.Density functional theory (DFT) calculations were employed as the computational tool to examine Pengfei Ou and Dr. Fanchao Meng for their kind assistance during the initial stage of my research.Many thanks to all my friends, especially Chuhong Wang, Tiantian Yin and Xun Du, who always
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".