The role of particle-to-cell interactions in dictating nanoparticle aided magnetophoretic separation of microalgal cells
Bibliographic record
Abstract
Successful application of a magnetophoretic separation technique for harvesting biological cells often relies on the need to tag the cells with magnetic nanoparticles. This study investigates the underlying principle behind the attachment of iron oxide nanoparticles (IONPs) onto microalgal cells, Chlorella sp. and Nannochloropsis sp., in both freshwater and seawater, by taking into account the contributions of various colloidal forces involved. The complex interplay between van der Waals (vdW), electrostatic (ES) and Lewis acid-base interactions (AB) in dictating IONP attachment was studied under the framework of extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) analysis. Our results showed that ES interaction plays an important role in determining the net interaction between the Chlorella sp. cells and IONPs in freshwater, while the AB and vdW interactions play a more dominant role in dictating the net particle-to-cell interaction in high ionic strength media (≥100 mM NaCl), such as seawater. XDLVO predicted effective attachment between cells and surface functionalized IONPs (SF-IONPs) with an estimated secondary minimum of -3.12 kT in freshwater. This prediction is in accordance with the experimental observation in which 98.89% of cells can be magnetophoretically separated from freshwater with SF-IONPs. We have observed successful magnetophoretic separation of microalgal cells from freshwater and/or seawater for all the cases as long as XDLVO analysis predicts particle attachment. For both the conditions, no pH adjustment is required for particle-to-cell attachment.
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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 | 0.000 |
| 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 | 0.000 | 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".