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Record W2000522161 · doi:10.3791/52327

Surface Potential Measurement of Bacteria Using Kelvin Probe Force Microscopy

2014· article· en· W2000522161 on OpenAlexafffund
Eric Birkenhauer, Suresh Neethirajan

Bibliographic record

VenueJournal of Visualized Experiments · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicForce Microscopy Techniques and Applications
Canadian institutionsUniversity of Guelph
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of CanadaResearch and Innovation Foundation
KeywordsKelvin probe force microscopeVolta potentialMicroscopyAdhesionAtomic force microscopyBiophysicsNanotechnologySubstrate (aquarium)Materials scienceMembraneScanning probe microscopyChemistryComposite materialOpticsBiologyPhysicsBiochemistry

Abstract

fetched live from OpenAlex

Surface potential is a commonly overlooked physical characteristic that plays a dominant role in the adhesion of microorganisms to substrate surfaces. Kelvin probe force microscopy (KPFM) is a module of atomic force microscopy (AFM) that measures the contact potential difference between surfaces at the nano-scale. The combination of KPFM with AFM allows for the simultaneous generation of surface potential and topographical maps of biological samples such as bacterial cells. Here, we employ KPFM to examine the effects of surface potential on microbial adhesion to medically relevant surfaces such as stainless steel and gold. Surface potential maps revealed differences in surface potential for microbial membranes on different material substrates. A step-height graph was generated to show the difference in surface potential at a boundary area between the substrate surface and microorganisms. Changes in cellular membrane surface potential have been linked with changes in cellular metabolism and motility. Therefore, KPFM represents a powerful tool that can be utilized to examine the changes of microbial membrane surface potential upon adhesion to various substrate surfaces. In this study, we demonstrate the procedure to characterize the surface potential of individual methicillin-resistant Staphylococcus aureus USA100 cells on stainless steel and gold using KPFM.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.220
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.414
Teacher spread0.384 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations17
Published2014
Admission routes2
Has abstractyes

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