Spatially Resolved Spectro-Electrochemistry Using Soft X-Ray Scanning Transmission X-Ray Microscopy
Bibliographic record
Abstract
We are using soft X-ray scanning transmission X-ray microscopy (STXM) [1] at CLS beamline 10ID1 (SM) and at ALS beamline 5.3.2.2 to investigate Cu deposition onto, and stripping from a Au surface. Electrochemical reduction of Cu(II) under acid conditions is a commonly used process to deposit copper for integrated circuit interconnects. The reduction of Cu(II) to Cu(0) proceeds via a Cu(I) intermediate, which can be detected under neutral or basic conditions. Cu 2p and O 1s spectromicroscopy is used to analyze initial and final states, follow the process in situ, and search for intermediate species. The apparatus and techniques for spectro-electrochemical-microscopy will be described. Ex situ and in situ deposition & stripping of copper from CuCl2(aq) and CuSO4(aq) electrolytic solutions will be described. Progress towards an in situ flow electrochemical cell and a faster response time for kinetics measurements will be presented. STXM performed on BL 10ID1 at CLS and on BL 5.3.2.2 at ALS. Research supported by NSERC and the Catalyst Research for Polymer Electrolyte Fuel Cells (CaRPE-FC) network. [1] A.P. Hitchcock, Soft X-ray Imaging and Spectromicroscopy in Handbook on Nanoscopy, eds. G. Van Tendeloo, D. Van Dyck and S. J. Pennycook 2012. (Wiley) Figure 1
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".