Corrosion of UNS N06625 under Batch-Mode Biomass Supercritical Water Gasification (SCWG) of Lignin
Bibliographic record
Abstract
Abstract Supercritical water gasification (SCWG) is a thermochemical conversion technology developed to transform various feedstocks, such as raw forest biomass materials, crude bio-oils and bio-wastes, into syngas (a combination of CO and H2) for clean energy production. Despite the intensive research efforts that have been applied on the development of SCWG technology, the optimal SCWG operating parameters (temperature, pressure, and biomass/water ratio, etc.) are not well defined because of the complexity of feedstock types and conversion reactor configurations (batch or continuous mode). Moreover, little information is available to determine which alloys are suitable for the reactor construction in a long-term safe and cost-effective manner. This study investigated the corrosion of UNS N06625 under the catalytic SCWG conversion of lignin using standard high temperature autoclave testing methodology and XRD characterization of the formed corrosion products. It was found that the addition of NaOH catalyst resulted in a remarkable increase in H2 production. After 12 cycle exposures to the catalytic SCWG environment, the corrosion layer formed on the alloy was composed of Cr2O3 and Ni3S2. Surprisingly, the addition of NaOH led to a positive weight change instead of weight loss as that occurred in non-catalytic SCWG processes. Further works, such as accurate weight loss measurements and SEM/FIB/TEM characterizations of corrosion layer, are needed to advance the understanding of how the alloy corroded under the catalytic SCWG of lignin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.001 | 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 teacher head, 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".