Silicon Doping Titanium Dioxide to Produce Highly Conductive and Durable Catalyst Supports for Fuel Cells
Bibliographic record
Abstract
The demand for clean energy has increased significantly over the last few decades with technological advancements. Proton exchange membrane fuel cells (PEMFCs) are one such advancement with reportedly high efficiencies. Indeed, extensive research and development on all aspects regarding this technology has been done over the past two decades. However, this technology still requires more advanced electrode materials. Platinum (Pt), which exhibits high activity in the oxygen reduction reaction (ORR), is the most common and efficient electrocatalyst used in anode and cathode materials in PEMFCs. Pt is a noble metal, expensive and limited in supply. In order to maximize the efficiency of the Pt catalyst, supporting materials onto which the Pt catalyst is dispersed can play a crucial role. Traditionally, Pt nanoparticles (NPs) are dispersed onto high surface area carbonaceous materials (Pt/C) to maximize the efficiency of Pt catalyst. However, carbon is known to suffer from electrochemical degradation (ie. carbon corrosion) under harsh conditions of PEMFC, resulting in either/or Pt agglomeration and dissolution, decreasing the electroactivity of Pt catalyst. Metal oxides, such as TiO2, Nb2O5, and SiO2, have been extensively studied as alternative fuel cell catalyst supports to carbon-based materials for the ORR. Metal oxides are cheap, corrosion-resistant, and possess a large surface area. Unfortunately, these metal oxides are semi-conductive, making them impractical for fuel cell materials. Recently, there has been great interest to develop metal oxides with high electrical conductivity for fuel cell application. Among all conductive metal oxides, titanium suboxides (TixO2x-1) have shown great promise as replacements for carbon-supporting material in fuel cells. Herein, we are reporting an advanced, highly conductive metal oxide through Si-doping TiO2, yielding Ti3O5-Si (hereinafter referred to as TOS). The resulting Pt/TOS catalyst support exhibits remarkable electroactivity and durability toward the oxygen reduction reaction (ORR). Also, under advanced stability protocols including startup-shutdown and load-cycling, Pt/TOS demonstrates high stability and durability compared to commercial Pt/C (see Figure 1). This enhanced electroactivity and stability of Pt/TOS is attributed to the strong metal-support interaction (SMSI) occurring through electron donation from TOS support to the Pt nanoparticles. Figure 1
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".