Electrocatalysis and photoelectrochemistry based on functional nanomaterials
Bibliographic record
Abstract
Catalysis plays a key role in chemical production, energy processing, air purification, water treatment, food processing, and the life sciences. Nanostructured materials with high surface areas and some unique properties have received widespread interest in electrocatalysis and photocatalysis. Recently, the author’s research team has designed and studied a variety of novel functional nanomaterials. This review article is derived from the author’s 2013 Canadian Catalysis Lectureship Award Lecture and focuses primarily on the electrocatalytic activities of platinum- and palladium-based nanomaterials and the development of TiO2-based nanostructured photocatalysts. Palladium possesses several exceptional properties that may enable promising applications in hydrogen detection, purification, and storage. The significant roles of palladium-based nanomaterials in facilitating the growth of a hydrogen economy are addressed. As platinum-based catalysts are vital to the development of fuel cells and sensors, the design of high-performance platinum-based electrocatalysts is highlighted. Additionally, TiO2 is considered to be one of the most promising photocatalysts due to its nontoxicity, high stability, and cost effectiveness. The modification of TiO2 nanomaterials to achieve visible light response is discussed as well. It is anticipated that the development of advanced functional nanostructured catalysts will further improve the efficiency and reduce the cost of electrochemical and photochemical processes, making them more attractive in addressing the pressing global energy and environmental issues.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".