MétaCan
Menu
Back to cohort
Record W2796370271 · doi:10.1063/1.5023748

Ions, metabolites, and cells: Water as a reporter of surface conditions during bacterial growth

2018· article· en· W2796370271 on OpenAlexafffund
Tasha A. Jarisz, Sarah Lane, Lea Gozdzialski, Dennis K. Hore

Bibliographic record

VenueThe Journal of Chemical Physics · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaBritish Columbia Knowledge Development FundUniversities Space Research AssociationUniversity of Victoria
KeywordsIonic strengthAqueous solutionSurface chargeIonIonic bondingMaterials scienceSurface waterSurface (topology)Chemical engineeringMoleculeChemical physicsChemistryAnalytical Chemistry (journal)ChromatographyPhysical chemistryOrganic chemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Surface-specific nonlinear vibrational spectroscopy, combined with bulk solution measurements and imaging, is used to study the surface conditions during the growth of E. coli. As a result of the silica high surface charge density, the water structure at the silica-aqueous interface is known to be especially sensitive to pH and ionic strength, and surface concentration profiles develop that can be appreciably different from the bulk solution conditions. We illustrate that, in the presence of growing cells, a unique surface micro-environment is established as a result of metabolites accumulating on the silica surface. Even in the subsequent absence of the cells, this surface layer works to reduce the interfacial ionic strength as revealed by the enhanced signal from surface water molecules. In the presence of growing cells, an additional boost in surface water signal is attributed to a local pH that is higher than that of the bulk solution.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.243
Teacher spread0.235 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of Chemical PhysicsSame topicSpectroscopy and Quantum Chemical StudiesFrench-language works237,207