Two Routes for Sonochemical Synthesis of Pt-Nanoparticles
Bibliographic record
Abstract
To improve the performance of electrochemical hydrogen conversion devices such as electrolyzers and fuel cells, more efficient catalyst materials must be developed. As the available surface area is key to the performance of a catalyst, high surface area nanoparticles are required. The synthesis of such nanostructured catalysts is predominantly based on chemical reduction with a strong reducing agent like sodium borohydride. However, it is difficult to achieve the desired particle properties without the use of additives such as surfactants or stabilizers. In order to achieve more control over the nucleation and growth stage of the nanoparticles, we can replace the chemical reduction route by sonochemical reduction. In sonochemistry, high power ultrasound is used to generate reducing radicals which allow for controlled reduction of metal precursors. The radical generation rate is strongly linked to the nucleation and growth of the resulting nanoparticles and can easily be tuned by adjusting the ultrasonic frequency and power. Ultrasound therefore offers an easy and reproducible way of tuning the size of nanoparticles. In our work we have compared the size, shape, and morphology of Pt-nanoparticles synthesized sonochemically and with chemical reduction to see if the sonochemical route indeed offers better control during nucleation and growth. TEM-images along with X-ray diffractograms revealed that the sonochemical route gives smaller and more monodisperse particles compared to chemical reduction. In addition, the particle shape was found to be spherical with the sonochemical route, whereas chemical reduction resulted in many different shapes. The better and easier control over synthesis parameters exhibited by the sonochemical route can therefore be utilized to produce nanoparticles of very similar physical properties. We also showed that the catalytic properties of these nanoparticles towards hydrogen evolution were very similar further strengthening the sonochemical route when it comes to development of catalyst materials. A comparison between high frequency (408 kHz) and low frequency (20 kHz) ultrasound was also made to determine the impact of the higher mechanical effects expected at lower frequencies. No significant differences in particle size and morphology were found, but agglomerate sizes were found to decrease at lower frequencies. In addition, it was found that extensive probe erosion occurs at 20 kHz resulting in contamination of the nanoparticle solution. In fact, it was shown that the eroded particles act as nucleation sites for Pt-nanoparticles and increased reduction rates were observed. No such particle erosion happened at higher frequencies which led us to conclude that direct sonication at low ultrasonic frequencies in general must be avoided to prevent contamination of your system. This is important in all applications of low frequency (20 kHz) ultrasound, whether it be sonochemistry, dispersion of colloids, or ink preparation for electrochemical characterization.
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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 | 0.000 |
| 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) | 0.000 | 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".