Stainless Steel As a Catalyst for the Total Deoxygenation of Glycerol and Levulinic Acid in Aqueous Acidic Medium
Bibliographic record
Abstract
Exposing 316 Stainless Steel pressure reactor bodies to an aqueous Brønstedt acidic solution (trifluoromethane sulfonic acid) at elevated temperatures (100−250 °C) under reducing atmosphere (hydrogen gas at 800 psi) leads to the formation of insoluble inorganic precipitates, identified as mixed chromium oxides by scanning electron microscopy X-ray fluorescence (SEM-XRF). A catalytically active metal surface is generated, that is, under these conditions the <100 Ǻ thick chromium oxide layer that normally passivates 316 Stainless Steel (316SS) against corrosion is etched away, and the reactor body itself becomes an active hydrogenation catalyst. The effect is specific to aqueous acidic medium and therefore water-soluble substrates as encountered in biomass conversion, for example, sugar alcohols and levulinic acid, which can be deoxygenated to the corresponding alkanes and alkenes using only a Brønstedt acid and the reactor body as the catalyst. Control experiments in several different 316SS reactors built by different manufacturers from different batches of 316SS as well as inductively coupled plasma optical emission spectroscopy (ICP-OES) and mass spectrometry (ICP-MS) analysis of the chromium oxide precipitates formed and steel samples from the reactor body itself indicate that the catalytic activity is not caused by trace amounts of ruthenium or another hydrogenating metal such as Re, Rh, Ir, Pd, or Pt. The observed catalytic activity scales with the concentration of acid and the addition of 316SS added to the reaction mixture as a powder conclusively establishing 316SS as the active catalyst.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".