Contamination and carryover free handling of complex fluids using lubricant-infused pipette tips
Bibliographic record
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 and viscous fluids are involved. Moreover, hydrophobicity of pipette tips can result in hydrophobic adsorption of biomolecules, causing inaccuracies and loss in precision during pipetting. 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). The presence of FS groups on the tips is confirmed by x-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FTIR) tests. After lubrication of the tips through a fluorinated lubricant, the omniphobicity and repellent behaviour of the tips drastically enhanced which are revealed via static and hysteresis contact angle measurements. The repellency 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. We also demonstrate the lubricant-infused tips reduce bacteria contamination of the inner lumen by 3 to 6-log (over 99%, depending on the tip size) after pipetting up and down the bacteria solution.
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.
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 | 0.000 | 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 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".