Leveraging flexible pipette-based tool changes to transform liquid handling systems into dual-function sample preparation and imaging platforms
Bibliographic record
Abstract
In soft materials synthesis, rapid self-assembly and poor mechanical strength often limit the applicability of experimental characterization techniques. This limitation arises because transferring these materials to a suitable imaging platform is either too slow to capture the process of interest or impossible to safely transfer from the synthesis vessel to the characterization. In addition, the variable nature of these materials requires many experiments to understand the underlying structure-property relationships that govern these materials. In this work we present a new hardware platform that integrates simultaneous pipetting and in-situ imaging using the Opentron OT-2 liquid handling robot. A 3D printed adapter features two cylindrical openings, one containing the pipette tip to gantry adapter, and the other a USB camera. When the gantry picks up the pipette tip, the entire apparatus is lifted, allowing the camera to be used. This system enables real-time monitoring and characterization of dynamic processes, such as hydrogel crosslinking, without manual intervention. We used this system to characterize ionically crosslinked hydrogels, and monitored their properties over time, in a high-throughput and combinatorial manner. Although hydrogels were used as a proof-of-concept, this platform has broader applications in materials research, including crystallization dynamics, polymerization kinetics, and drug delivery system development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".