Antibacterial Coating on Filtration Membranes for Treatment of Cutting Fluid
Bibliographic record
Abstract
Abstract: Cutting fluids has greater significance in manufacturing processes to ensure work-piece quality, to reduce tool wear, and to improve process productivity. The specific chemical composition of an applied coolant should be strongly dependent on the scope of application. Even small changes such as presence of microorganisms such as Staphylococcus, Streptococcus, Pseudomonas, Alcaligenes etc. can influence the performance of cutting fluid and introduce risk of various skin diseases to the operator in the manufacturing processes considerably. In this project the antibacterial coating is brought into use by coating a thin layer of silver nano particles on a polypropylene filtration membrane. A coated and non-coated membrane was placed separately on the cutting fluid sump of a vertical milling machine. 10litres of cutting fluid with a Servo cut Soil content of 5% and distilled water with a concentration of 95% were used in the machine while a milling process was carried out. Then a sample of cutting fluid (about 250mL) was taken from the tank and preserved for testing purposes after passing through the filter membranes. On the same cutting fluid, the machine was run for another two days, and a third sample was taken at the end of the fourth day. The samples collected were tested at Azyme Biosciences Pvt Ltd for bacterial count (CFU/ml) and the results showed that the CFU/ ml in the sample filtered through the non-coated polypropylene filtration membrane was higher than in the sample filtered via coated polypropylene filtration membrane.
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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 | 0.001 | 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".