Towards scaling up the sonochemical synthesis of Pt-nanocatalysts
Bibliographic record
Abstract
Large scale production of electrocatalysts for electrochemical energy conversion devices such as proton exchange membrane fuel cells must be developed to reduce their cost. The current chemical reduction methods used for this synthesis suffer from problems with achieving similar particle properties such as particle size and catalytic activity when scaling up the volume or the precursor concentration. The continuous production of reducing agents through the sonochemical synthesis method could help maintain the reducing conditions (and also the particle properties) upon increasing the reactor volume. In this work we demonstrate that the reducing conditions of Pt-nanoparticles are indeed maintained when the reactor volume is increased from 200 mL to 800 mL. Similar particle sizes, 2.1(0.3)nm at 200 mL and 2.3(0.4)nm at 800 mL, and catalytic activities towards the oxygen reduction reaction (ORR) are maintained as well. The reducing conditions were assessed through TiOSO4 dosimetry, sonochemiluminesence imaging, acoustic power measurements, and Pt(II) reduction rate measurements. Cyclic voltammetry, CO-stripping, hydrogen evolution measurements, ORR measurements, and electron microscopy were used to evaluate the catalytic activity and particle size. The similar particle properties displayed from the two reactor volumes suggest that the sonochemical synthesis of Pt-nanoparticles is suitable for large scale production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".