Transient Absorption Microscopy Maps Spatial Heterogeneity and Distinct Chemical Environments in Photocatalytic Carbon Nitride Particles
Bibliographic record
Abstract
Abstract Limitations in solar energy conversion by photocatalysis typically stem from poor underlying charge carrier properties. Transient Absorption (TA) reveals insights on key photocatalytic properties such as charge carrier lifetimes and trapping. However, on the microsecond timescale, these measurements use relatively large probe sizes ranging in millimetres to centimetres which averages the effect of spatial heterogeneity at smaller length scales. A home‐built Transient Absorption Microscopy (TAM) setup is reported and used to study single particles of carbon nitride (CNx), an emerging photocatalyst. For the first time, to the best of the authors' knowledge, µs‐s timescales are explored within individual particles to gain a more complete understanding of their photophysics. The dynamics of trapped charges are monitored, enabling measurement and quantification of heterogeneity in the transient absorptance signal of individual CNx particles and within them. Particle‐to‐particle heterogeneity in the trapped charge density is observed, while spatial heterogeneity in lifetimes within a particle is revealed using a smaller probe beam with a ≈5 µm diameter. Overall, the observations suggest that contributions from different local environments independently influence charge trapping at different timescales. TAM on the micron and microsecond spatiotemporal resolution will aid in tackling design questions about optimal chemical environments for the promotion of photoactivity.
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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".