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, such the synthesis of hydrogels, the rapid self-assembly and poor mechanical strength of these transient materials systems limit the applicability of many useful experimental characterization techniques. This limited applicability is because often the act of transferring these materials to a suitable imaging platform is either too slow to capture the process of interest or it is impossible to safely transfer the material from the synthesis vessel to the characterization equipment. In addition, the variable nature of these materials requires many experiments to be conducted to understand the underlying structure-property relationships that govern these transient materials. In this work we present a new hardware platform to address this experimental gap. This hardware integrates simultaneous pipetting and in-situ imaging using the Opentron OT-2 liquid handling robot. The 3D printed apparatus acts as an adapter with two cylindrical openings, one containing the pipette tip to gantry adapter, and the other containing a USB camera. When the pipetting gantry picks up the pipette tip, the entire apparatus is lifted, which allows the camera to be used during the operation. This system enables real-time monitoring and characterization of dynamic processes, such as hydrogel crosslinking, without manual intervention. We used this system to characterize several ionically crosslinked hydrogels, and monitored their properties over time, in a high-throughput and combinatorial manner. Although ionically crosslinked hydrogels were used as a proof-of-concept, this platform has potential applications across various materials systems, 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".