Critical review: Plasma-surface reactions and the spinning wall method
Bibliographic record
Abstract
This article reviews methods for studying reactions of atoms and small molecules on substrates and chamber walls that are immersed in a plasma, a relatively unexplored, yet very important area of plasma science and technology. Emphasis is placed on the “spinning wall” technique. With this method, a cylindrical section of the wall of the plasma reactor is rotated, and the surface is periodically exposed to the plasma and then to a differentially pumped mass spectrometer, to an Auger electron spectrometer, and, optionally, to a beam of additional reactants or surface coatings. Reactants impinging on the surface can stick and react over time scales that are comparable to the substrate rotation period, which can be varied from ∼0.5 to 40 ms. Langmuir–Hinshelwood reaction probabilities can be derived from a measurement of the absolute desorption product yields as a function of the substrate rotation frequency. Auger electron spectroscopy allows the plasma-immersed surface to be monitored during plasma operation. This measurement is critical, since wall “conditioning” in the plasma changes the reaction probabilities. Mass spectrometer cracking patterns are used to identify simple desorption products such as Cl2, O2, ClO, and ClO2. Desorption products also produce a measurable pressure rise in the second differentially pumped chamber that can be used to obtain absolute desorption yields. The surface can also be coated with films that can be deposited by sputtering a target in the plasma or by evaporating material from a Knudsen cell in the differentially pumped wall chamber. Here, the authors review this new spinning wall technique in detail, describing both experimental issues and data analysis methods and interpretations. The authors have used the spinning wall method to study the recombination of Cl and O on plasma-conditioned anodized aluminum and stainless steel surfaces. In oxygen or chlorine plasmas, these surfaces become coated with a layer containing Si, Al, and O, due to slow erosion of the reactor materials, in addition to Cl in chlorine plasmas. Similar, low recombination probabilities were found for Cl and O on anodized Al versus stainless steel surfaces, consistent with the similar chemical composition of the layer that forms on these surfaces after long exposure to the plasma. In chlorine plasmas, weakly adsorbed Cl2 was found to inhibit Cl recombination, hence the Cl recombination probability decreases with increasing Cl2-to-Cl number density ratios in the plasma. In mixed Cl2/O2 plasmas, Cl and O recombine to form Cl2 and O2 with probabilities that are similar to those in pure chlorine or oxygen plasmas, but in addition, ClO and ClO2 form on the surface and desorb from the wall. These and other results, including the catalytic enhancement of O recombination by monolayer amounts of Cu, are reviewed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.010 |
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".