Evaluation of the Catalytic Activity of Various 5Ni/Ce<sub>0.5</sub>Zr<sub>0.33</sub>M<sub>0.17</sub>O<sub>2-δ</sub> Catalysts for Hydrogen Production by the Steam Reforming of a Mixture of Oxygenated Hydrocarbons
Bibliographic record
Abstract
A portfolio of nickel-based catalysts with nominal composition 5Ni/Ce 0.5 Zr 0.33 M 0.17 O 2-δ [where M is the promoter element(s) selected from Mg, Ca, Y, La, CaMg, or Gd] was prepared and examined for their catalyst activity for the steam reforming of an equimolar liquid mixture of six oxygenated hydrocarbons (ethanol, 1-propanol, 1-butanol, lactic acid, ethylene glycol, and glycerol) at different temperatures in the range of 500–700 °C at atmospheric pressure in a packed-bed tubular reactor (PBTR). The Ce 0.5 Zr 0.33 M 0.17 O 2-δ supports were prepared by a surfactant-assisted route. Nickel was impregnated over the supports by the wet-impregnation method. The physicochemical and textural characteristics of the catalysts were evaluated by means of various characterization techniques. Among the portfolio of catalysts evaluated, the ones containing Ca, CaMg, Mg, or Gd promoter exhibited steady activity at all of the temperatures evaluated. To deconvolute the thermal effect from the catalytic effect, a number of thermal noncatalytic experiments were conducted in the absence of any catalyst at different temperatures (450–700 °C). Correlations have also been established between the catalytic performance and catalyst characteristics to identify the parameters that influence the catalytic behavior, which could aid in catalyst improvement. It was found that catalytic activity increased with an increase in the active metal reducibility, ratio of pore volume/surface area (PV/SA), and active metal dispersion but decreased with an increase in the carbon propensity factor (CPF).
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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) | 0.000 | 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) | 0.000 | 0.000 |
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".