Strong and small: strong cation-exchange solid-phase extractions using porous polymer monoliths on a digital microfluidic platform
Bibliographic record
Abstract
We present the first method for digital microfluidics-based strong cation-exchange solid-phase extractions. Digital microfluidics is a microscale fluid handling technique in which liquid droplets are actuated over an array of electrodes by electrodynamic forces. Strong cation exchange has gained considerable importance in the field of proteomics as a separation mode for protein and peptide extractions. The marriage of these two techniques is achieved by incorporating sulphonate-functionalised porous polymer monolith discs onto digital microfluidic chips. By manipulating sample and solvent droplets onto and off of these porous polymer monoliths, proteins and peptides are extracted by controlling solution pH and ionic strength. This novel microscale extraction method has efficiency comparable to commercially available strong cation-exchange ZipTips and is highly effective for sample cleanup. We anticipate that this digital microfluidic strong cation-exchange extraction technique will prove useful for microscale proteomic analyses and other applications requiring separation of cationic compounds.
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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".