Comparative Analysis of Conventional Cell Lysis Techniques to Electrochemical Cell Lysis using the Liquid Microjunction Surface Sampling Probe
Bibliographic record
Abstract
Cell lysis is a critical step in the analysis of cellular components, influencing the efficiency, reproducibility, and integrity of biomolecular extractions. Conventional lysis techniques, including mechanical and chemical methods, often present trade-offs between throughput, scalability, and sample integrity. Mechanical approaches such as bead milling and sonication can efficiently disrupt cells but suffer from issues such as back-mixing and biomolecule degradation. Chemical lysis techniques, while effective at preserving nucleic acids, require additional purification steps to remove residual chemicals that may interfere with downstream analyses. Electrochemical lysis (ECL) has emerged as a promising alternative, leveraging the electrochemical lysis of phosphate buffered saline (PBS) to induce membrane disruption at low potentials (2 to 5 V). ECL minimizes sample perturbation and aligns with green chemistry principles by reducing solvent and reagent consumption. This study evaluates ECL alongside conventional cell lysis methods using microbiological, electrochemical, and mass spectrometry (MS) analyses, using the Liquid Microjunction Surface Sampling Probe (LMJ SSP) for MS data acquisition. Cell lysates prepared using ECL were more reproducible samples abundant in diverse biomolecules compared to conventional methods. The integration of ECL with the LMJ SSP- MS has enabled rapid biochemical characterization with minimal sample preparation, direct sample introduction, and improved workflow efficiency.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".