Modeling and Experimental Examination of the Solonitsyn Memory Effect on the Surface of Wide Band Gap Metal Oxides
Bibliographic record
Abstract
When the surface of a solid semiconductor or dielectric metal oxide (or other) specimen is preirradiated, the solid often retains its photochemical activity after termination of irradiation through formation of long-lived surface-active adsorption centers. This effect has two origins, viz., the so-called Kugel'sberg memory effect and the Solonitsyn memory effect. The former denotes preirradiation in the presence of the adsorbate molecules, whereas the latter refers to preirradiation in vacuo followed by subsequent introduction of adsorbate molecules into the reactor. This article reports results of detailed studies on the Solonitsyn memory effect in gas/solid heterogeneous systems with respect to photostimulated adsorption (i.e. reductive or oxidative adsorption) of molecular oxygen, molecular hydrogen, and methane on the surface of a dielectric metal oxide such as zirconia. The memory effect has been quantified for several metal oxides and alkali halides by means of an experimentally determined postadsorption memory coefficient, η( t ), which defines the fraction of long-lived photoadsorption centers with respect to the total number of both long-lived and short-lived surface centers of photoadsorption generated for a time of irradiation, t . A simple model is proposed to explain the experimental data.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".