Phosphonic acid functionalized silicas for intermediate temperature proton conduction
Bibliographic record
Abstract
Highly proton conductive silicas with phosphonic acid functionalization were synthesized by co-condensation of diethylphosphatoethyltriethoxysilane (DPTS) and tetraethoxysilane in a sol-gel process, followed by acidification of the phosphonate groups. These functionalized silicas with various phosphonic acid contents were extensively characterized to examine their structures and properties; in particular their intermediate temperature proton conductivity at 100–150 °C were systematically investigated under a variety of relative humidity (RH) conditions. The prepared samples have a mesoporous or nonporous structure depending on the DPTS amount used in the synthesis, and show high thermal stability under inert and oxidative atmospheres. We found that the present silicas still exhibit water-dependent proton conduction, but their conductivity under low humidity conditions has been significantly enhanced by up to two orders of magnitude compared to those phosphonic acid functionalized silicas previously reported. Herein, the highest conductivity has been obtained at 150 °C ranging from 4.4 × 10−4 S cm−1 at 20% RH to 0.031 S cm−1 at 100% RH. In general, proton conductivity is largely influenced by the content of phosphonic acid and the porous structure of the materials. Notably, the uniform mesostructure with a high surface area was found to greatly improve the proton conductivity at low humidity. The vehicle mechanism dominates the proton conduction at high humidity, whereas the conductivity at low humidity is likely a consequence of the structure diffusion (the Grotthuss mechanism). In addition, these materials are insoluble in water, rendering a practical suitability for fuel cell applications.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".