Advanced Nanomaterials and Characterization Techniques for Photovoltaic and Photocatalysis Applications
Bibliographic record
Abstract
Conspectus Solar energy is one of the most promising energy sources to replace traditional fossil fuels due to its renewable and green features, which can be converted to electrical and chemical energy through photon-enabled applications. To improve the utilization efficiency of solar energy, solar energy “converters”, such as photovoltaic and photocatalytic systems, have been extensively studied. It is noteworthy that the common issues of narrow optical absorption and rapid charge carrier recombination limit solar energy utilization. The development of advanced functional nanomaterials plays a decisive role in addressing these issues. For instance, plasmonic nanomaterials with a localized surface plasmon resonance (LSPR) effect can effectively extend and enhance light absorption; heterojunction- and homojunction-based semiconductors can facilitate the spatial separation of electron–hole pairs. Therefore, rational design of functional nanomaterials through integrating plasmonic nanomaterials and creating heterojunctions and homojunctions can amplify their structural advantages, leading to the achievement of the state-of-the-art photon-conversion performance. Besides, the in-depth understanding of the relationship between materials and performance via advanced characterization techniques, such as high spatial-resolution imaging and in situ spectroscopy, provides a fundamental and solid basis for optimizing advanced functional materials in photon-enabled applications. Along with theoretical calculation and algorithm-driven data analysis during advanced characterizations, more quantified information can be obtained for deeper insights into physics. In this Account, we first summarize recent works in our research group on the rational design of advanced functional materials, including plasmonic metallic materials, plasmonic semiconductors, two-dimensional-material-based heterojunctions, and metal–organic-framework-based homojunctions, and their working mechanisms for the enhancement of photovoltaic and photocatalytic performance. We then show how we employed developed X-ray-based, electron-based, and spectroscopic techniques for characterizing elemental composition, materials structure, and physicochemical properties, which provides effective ways to resolve complex structures and processes and understand their underlying physics. Furthermore, we discuss the photogenerated charge carrier dynamics in solar cells and photocatalysis using in situ and time-resolved techniques, by underlining the use of these advanced techniques for specific materials. Then, we briefly introduce the algorithm-driven data analysis compiled in analytical techniques in our works to quantify materials information. Finally, we briefly present perspectives for addressing the challenges and fundamental issues as well as guidance for the future development of photon-enabled applications, e.g., the development of high-performance functional materials and advanced characterization techniques. This Account shows some ideas and directions for the rational design and optimization of advanced functional materials for various photon-enabled applications and for the proper utilization of advanced characterization techniques, which may provide guidance and prospects for future research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".