Common Pitfalls of Catalysis Manuscripts Submitted to Chemistry of Materials
Bibliographic record
Abstract
Many science and engineering journals focus on catalysis research, sometimes with “Catalysis” contained within the name of the journal. Chemistry of Materials also covers aspects of catalysis, as stated in its Scope Statement: “Among the areas of interest are inorganic and organic solid-state chemistry, composite materials, nanomaterials, biomaterials, thin films and polymers, especially when focused on the creation or innovative development of materials with novel and potentially useful optical, electrical, magnetic, catalytic, or mechanical properties”. Importantly, this statement emphasizes a focus on materials with innovative and potentially useful catalytic properties, not the catalytic reaction itself. As such, many authors of catalysis-relevant manuscripts submitted to Chemistry of Materials would not regard themselves as strongly affiliated with the core catalysis community as much as other chemistry subdisciplines. Consequently, these authors may not be familiar with the reporting standards in catalysis research, or they may not have experienced common pitfalls related to reporting catalytic properties of materials. Chemistry of Materials does not expect the same level of detail and depth when reporting catalytic data as journals that specialize in catalysis. There are some basic tenets authors should consider when submitting catalysis-relevant manuscripts to Chemistry of Materials, however, as failing to do so will likely lead to rejection, or at least a request for modification before further consideration of the manuscript. Some frequent issues experienced by the journal editors are addressed below, in brief, as guidance to authors for improving the quality of their manuscripts and the chances for a smoother review process.
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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 | 0.190 | 0.392 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 0.013 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".