Nanostructured Electrocatalysts for Energy and Environmental Applications
Bibliographic record
Abstract
This presentation will include two parts. In the first part, I will focus on our recent work on designing various novel Pt nanostructures as electrocatalysts for PEM fuel cells (PEMFCs). The short life-time and high cost of Pt catalyst are the main obstacles for the commercialization of PEMFCs. It is well accepted that the catalytic activity and durability of Pt catalysts are highly dependent on their morphology, and therefore the exploration of novel Pt nanostructures has become an area of considerable interest. To date, most studies have mainly focused on 0D nanoparticles of Pt. Very recently, 1D structures of Pt, such as nanowires (NWs), have emerged as a new type of promising fuel cell catalyst, exhibiting much enhanced performance compared to the commercially-used Pt/C nanoparticle catalysts. Here, I will systematically introduce our recent work on the green chemistry synthesis of 1D Pt NWs and their use as highly efficient electrocatalysts for PEMFCs. Specifically: (i) A facile method to synthesize Pt NWs (4 nm in diameter), which exhibit 3-times better activity and 5-fold better durability, for ORR, than the state-of-the-art commercial catalyst made of Pt nanoparticles; 1-3 (ii) PtNWs on Sn@CNT nanocable 3D electrodes; 4 (iii) Diameter control of Pt NWs grown on CNTs and N-doped CNTs; (iv) some very recent results will be presented as well. In the second part, I will report our recent work on waste water treatment. Environmental pollution is a global menace, and its magnitude is increasing day-by-day due to urbanization, heavy industrialization and the changing lifestyles of people. The demand for hydrogen peroxide (H 2 O 2 ) is booming since it is considered as one of the most environmentally friendly and versatile chemical oxidants available and has a wide range of applications, especially in waste water treatment (degradation of organic pollutants). In-situ generation of H 2 O 2 has attracted a growing interest since it avoids the cost and risks involved in the transportation and handling of concentrated H 2 O 2 . We developed a simple method to prepare two types of catalysts: Fe 3 O 4 /Printex and Fe 3 O 4 /graphene, which show promising activity for ORR in alkaline medium to generate H 2 O 2 . Further, both catalysts show excellent durability, and even more, the activity of Fe 3 O 4 /graphene increased after several hours durability test. These catalysts hold very promising potential applications in waste water treatment.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".