Unveiling competitions between carrier recombination pathways in semiconductors via mechanical damping
Bibliographic record
Abstract
The total rate of carrier recombination in semiconductors has conventionally been expressed using an additive model, r_total = Σr_i , which rules out the interactions between carrier recombination pathways. Here we challenge this paradigm by demonstrating pathway competitions using our newly developed light-induced mechanical absorption spectroscopy (LIMAS), which allows us to probe genuine recombination dynamics in semiconductors via mechanical damping. We show that the total recombination rate in zinc sulfide (ZnS), a model semiconductor material, follows a multiplicative weighting model, r_total \propto Πr_i ^(w_i) with Σw_i=1. Under both steady-state and switch-on illuminations, the weighting factors w_i for each recombination pathway-direct, trap-assisted, and sublinear-are dictated by the carrier generation mechanism: (i) interband transition favors direct recombination; (ii) single-defect level-mediated generation promotes trap-assisted recombination; (iii) generation involving multiple saturated defect levels gives rise to sublinear recombination. Upon light switch-off, localized state changes drive a dynamic evolution of w_i, altering pathway competitions. These findings reshape our fundamental understanding of carrier dynamics and provide a new strategy to optimize next-generation optoelectronic devices.
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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 | 0.001 |
| 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 | 0.001 |
| 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".