Bioinspired Repellent Pipette Tips with Low Retention Properties Prevent Contamination in Handling Biological Samples
Bibliographic record
Abstract
Abstract Cross-contamination of biological samples during handling and preparation, is a major issue in laboratory setups, leading to false-positives or false-negatives. Sample carryover residue in pipette tips contributes greatly to this issue. Most pipette tips on the market are manufactured with hydrophobic polymers that are able to repel high surface tension liquids, yet they lack in performance when low surface tension liquids are involved. This presents an obstacle for pipette tips as inaccuracies and loss in precision arise when low surface tension liquids such as viscous oils are pipetted. Here we propose the use of lubricant-infused surface (LIS) technology to achieve omniphobic properties in pipette tips. Using a versatile and simple design, the inner lumen of commercially available pipette tips was coated with a fluorosilane (FS) layer using chemical vapor deposition (CVD). We show that after lubricating the tips through simply pipetting up and down a fluorinated lubricant, the surface free energy of the tips drastically decreased enabling them to attain low retention properties. Contact angle measurements reveals that the treated pipette tips have enhanced omniphobic properties. The repellent behavior of the lubricant-infused pipette tips against physical adsorption is investigated through pipetting a food coloring dye as well as human blood samples and are compared to the untreated tips. The results show significantly less amount carryover residue when the lubricant-infused tips are utilized compared to commercially available ones.
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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 | 0.001 |
| 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) | 0.004 | 0.002 |
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".