Mono-Dispersed Pt/MWNTs: Growing Directly on Multiwall Carbon Nanotubes (MWNTs) Using NaBH4 as Reducing Agent for Component of Proton Exchange Membrane Fuel Cell (PEMFC)
Bibliographic record
Abstract
Synthesis of mono-dispersed Pt/MWCNTs has been performed. Platinum nanoparticles (Pt NPs) were grown directly on multiwall carbon nanotubes (MWCNTs) through sol-gel method using NaBH4 as reducing agent.       120 mg of activated MWCNT were weighed and then incorporated into the mixture (1) and sonicated for 2 hours to form the mixture (2). H2PtCl6 was weighed as much as 90 mg and dissolved into 45 mL of ethylene glycol until formed mixture (3). Solution (3) was dropwise every 3 seconds into the mixture (2). After that the mixture was distilled for 12 hours at a rate of 450 rpm. Subsequently the mixture was sonicated for 3 hours, then checked its pH, adjusting the desired pH to 4, 7, or 13 using the mixture 2M NaOH-ethylene glycol. The tests include SEM, EDS, XRD, and TEM for the morphologies and microstructures of the mono-dispersed Pt/MWCNT. The result of SEM observation and the analysis of the element using EDS found that the composite sample looked homogenous and contained element of C (MWCNT) and Pt (platinum). From the XRD shows that the composite Pt/MWCNT of the product synthesized without the reducing agent consists of three phases, namely C (MWCNTs), Pt (platinum), and H2PtH4, while the product synthesized using NaBH4 reducing agent consist of two phases, namely C (MWCNTs) and Pt (platinum ). The TEM image shows that the Pt NPs are spherical in size ~ 5 nm. Pt NPs appear to be attached on MWCNTs, either agglomerated or dispersed on the surface of MWCNTs. This paper will be compared between Pt/MWCNTs synthesis results with and without using NaBH4 reducing agent, as well as dispersed Pt NPs on MWCNTs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 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".