Hazardous Content Removal and Silver Nanoparticle Recovery from Liquid Radiography Waste Using Microwave Plasma
Bibliographic record
Abstract
The components of liquid radiography waste may be harmful or useful, depending on the composition of the raw material.This paper aims to remove the hazardous content from liquid radiography waste, while recoving the silver nanoparticles.The microwave plasma method was adopted to achieve this purpose.First, the conventional microwave oven was modified to generate the plasma in 100mL liquid radiogrpahy waste at 500W.The plasma generation time was varied for 1, 3, 4, 7, and 10min.In the treated liquid, the total suspended solid (TSS) was analyzed through vultraviolet spectrophotmetry, the silver (Ag) was examined through atomic absorption spectrophometory, and the biological oxygen demand (BOD) and chemical oxygen demand (COD) were discussed by titration method.In addition, the recovered silver was identified by X-ray diffraction (XRD) and scanning electron microscopy (SEM).The results show that the microwave plasma method reduced the TSS by 99.36%, the Ag by 92.73%, the pH by 3.42%, the BOD by 57.13%, and the COD by 57.14%;In addition, spherical shaped silver nanoparticles were indentified at some peaks with average crystallite size ranging from 2.64nm to 65.64nm.The research findings shed important new light on the recycling of liquid radiography waste.
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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".