The dataset of Preparation of Mn-V-O binary oxides and study on their aerobic oxidation desulfurization performance
Bibliographic record
Abstract
The data set includes FT-IR and XRD of the sample, XPS of the sample, SEM and element mapping of the sample, comparison of desulfurization performance between Mn-V-O catalyst and other catalysts, effect of reaction temperature on desulfurization effect, effect of oxygen flow rate on desulfurization rate, effect of catalyst dosage on desulfurization, desulfurization effect of different sulfides, analysis of recycling performance and stability of the catalyst, free radical capture and infrared spectrum of the product.The reaction temperature of the experimental samples, the amount of oxygen, the amount of catalyst, the effect of different sulfides on the desulfurization rate, the recycling performance, and the free radical capture experimental data were all completed by the WK-2D microcoulometric analyzer.The scanning electron microscope was performed by Philips XL 30 electron microscope, the FT-IR data was performed by NEXUS 870 Fourier transform infrared spectrometer, the XRD data was measured by D8 Advance X-ray diffractometer, and the XPS data was measured by K-Alpha XPS spectrometer. The electron paramagnetic resonance ( EPR ) test was performed on the Bruker-Emx Plus spectrometer, and the surface structure information of the sample was performed on the Micromeritics ASAP 2010 N2 adsorption-desorption automatic adsorption instrument.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.026 |
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".