Investigation of the potential effect of encapsulated metal nanoparticles on enhancement of thermophilic anaerobic digestion
Bibliographic record
Abstract
The present work investigates the enhancement effect of seven different catalysts made of Cu/SiO2, Pd/SiO2, Pt/SiO2, Ni/SiO2, Co/SiO2, Ag/SiO2 and Fe/SiO2 nanoparticles (NPs) on methane production during thermophilic anaerobic digestion. The tested NPs were synthesized by the sol-gel process and encapsulated in porous silica (SiO2) to prevent their coagulation and agglomeration. Transmission electron microscopy (TEM) pictures confirmed the specific morphologies of all seven catalysts. Then, these 7 NPs were tested first in batch experiments with acetate as a carbon substrate for bio-methane production. Ni/SiO2 and Co/SiO2 showed the best enhancement of methane production from acetate. From this part, both NPs were tested for bio-methane production on two different substrates: starch and glucose. With the starch substrate, the improvements of methane production were equal to 47% and 22%, respectively, for Ni- and Co/SiO2 compared to control sample. In the last part of this work, the influences of NP concentration and thermal pre-treatment applied to the NPs on bio-methane production from glucose were investigated. The results showed that all forms of nickel and cobalt NPs enhance the methane production, and their effect increased with the increase of their concentrations. The best sample was the calcined nickel NPs at a concentration of 10–4 mol L–1, leading to a methane production rate of 72.5% compared to the control.
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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) | 0.001 | 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 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".