Operando optical fiber monitoring of nanoscale and fast temperature changes during photo-electrocatalytic reactions
Bibliographic record
Abstract
In situ and continuous monitoring of thermal effects is essential for understanding photo-induced catalytic processes at catalyst's surfaces. However, existing techniques are largely unable to capture the rapidly changing temperatures occurring in sub-μm layers at liquid-solid interfaces exposed to light. To address this, a sensing system based on a gold-coated conventional single-mode optical fiber with a tilted fiber Bragg grating inscribed in the fiber core is proposed and demonstrated. The spectral transmission from these devices is made up of a dense comb of narrowband resonances that can differentiate between localized temperatures rapid changes at the catalyst's surface and those of the environment. By using the gold coating of the fiber as an electrode in an electrochemical reactor and exposing it to light, thermal effects in photo-induced catalysis at the interface can be decoded with a temperature resolution of 0.1 °C and a temporal resolution of 0.1 sec, without perturbing the catalytic operation that is measured simultaneously. As a demonstration, stable and reproducible correlations between the light-to-heat conversion and catalytic activities over time were measured for two different catalysis processes (linear and nonlinear). These kinds of sensing applications are ideally suited to the fundamental qualities of optical fiber sensors, such as their compact size, flexible shape, and remote measurement capability, thereby opening the way for various thermal monitoring in hard-to-reach spaces and rapid catalytic reaction processes.
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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".