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Record W4389121906 · doi:10.1021/acs.analchem.3c03716

Temperature Effect on the Electrochemical Current Response during Scanning Electrochemical Microscopy of Living Cells

2023· article· en· W4389121906 on OpenAlexafffund
Nikita Thomas, Dhésmon Lima, Dao Trinh, Sabine Kuss

Bibliographic record

VenueAnalytical Chemistry · 2023
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScanning electrochemical microscopyChemistryElectrochemistryKineticsAnalyteMicroscopyAnalytical Chemistry (journal)ElectrodeReaction rate constantConstant currentBiophysicsCurrent (fluid)NanotechnologyOpticsPhysical chemistryChromatographyMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Scanning electrochemical microscopy (SECM) is being used increasingly to monitor electrochemical processes at the interface of living cells and electrodes. This allows the detection and quantification of biomarkers that further the understanding of various diseases. Rapid SECM experiments are often carried out without monitoring the analyte solution temperature or are performed at room temperature. The reported research demonstrates that temperature control is crucial during SECM imaging of living cells to obtain reliable data. In this study, a SECM-integrated thermostatic ring on the sample stage enabled imaging of living biological cells in a constant height mode at various temperatures. Two-dimensional line scans were conducted while scanning single Adenocarcinoma Cervical cancer (HeLa) cells. Numerical modeling was carried out to evaluate the effect of the temperature on the electrochemical current response of living cells to compare the apparent heterogeneous rate constant ( k 0 ), representing cellular reaction kinetics. This study reveals that even slight temperature variations of approximately 2 °C affect the reaction kinetics of single living cells, altering the measured current during SECM.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes2
Has abstractyes

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