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Electrical Lysis: Dynamics Revisited and Advances in On-chip Operation

2013· review· en· W2083459281 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCritical Reviews in Biomedical Engineering · 2013
Typereview
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsUniversity of OttawaCarleton University
Fundersnot available
KeywordsLysisComputer scienceSonicationNanotechnologyMiniaturizationWorkflowBiochemical engineeringProcess engineeringChemistryMaterials scienceEngineeringBiochemistryChromatography

Abstract

fetched live from OpenAlex

Electrical lysis (EL) is the process of breaking the cell membrane to expose the internal contents under an applied high electric field. Lysis is an important phenomenon for cellular analysis, medical treatment, and biofouling control. This paper aims to review, summarize, and analyze recent advancements on EL. Major databases including PubMed, Ei Engineering Village, IEEE Xplore, and Scholars Portal were searched using relevant keywords. More than 50 articles published in English since 1997 are cited in this article. EL has several key advantages compared to other lysis techniques such as chemical, mechanical, sonication, or laser, including rapid speed of operation, ability to control, miniaturization, low cost, and low power requirement. A variety of cell types have been investigated for including protoplasts, E. coli, yeasts, blood cells, and cancer cells. EL has been developed and applied for decontamination, cytology, genetics, single-cell analysis, cancer treatment, and other applications. On-chip EL is a promising technology for multiplexed automated implementation of cell-sample preparation and processing with micro- or nanoliter reagents.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.306
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it