Antibacterial Coating on Filtration Membranes for Treatment of Cutting Fluid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it