Liquid-Handling Technology and the Method of Electrostatic Drop Transfer to Improve Dispensing Performance
Bibliographic record
Abstract
Liquid handling is a critical component of highly parallel processes such as high-throughput screening (HTS) and genomic analysis. Such processes require the use of smaller sample volumes and the ability to dispense without contact, driving the development of a variety of liquid-handling technologies to meet these needs. Such technologies have associated advantages and disadvantages, which makes choice of the right system application dependent. Syringe technology remains the most cost effective and versatile. It suffers, however, from the inability to dispense submicroliter volumes without contact. We provide a brief overview of liquid-handling technology, and present a new method of sample transfer based on electrostatic forces (Jetha, N. N.; Marziali, A. Electrostatic device for active transfer of submicroliter samples from syringe pipettors. BioTechniques. 2006, 40, 148–151) that can be incorporated into liquid-handling systems, enabling highly accurate and repeatable non-contact dispensing of submicroliter volumes. (JALA 2006;11:278–80)
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".