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Effects of pH and acid concentration on erosive dissolution of enamel, dentine, and compressed hydroxyapatite

2010· article· en· W2123117664 on OpenAlexaff
R.P. Shellis, Michele E. Barbour, Sian B Jones

Bibliographic record

VenueEuropean Journal Of Oral Sciences · 2010
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsDissolutionEnamel paintChemistryCitric acidNuclear chemistryMineralDentistryMineralogyBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The aims of this study were to determine the effects of pH and acid concentration on the dissolution of enamel, dentine, and compressed hydroxyapatite (HA) in citric acid solutions (15.6 and 52.1 mmol l(-1) ; pH 2.45, 3.2, and 3.9), using a pH-stat system. After an initial adjustment period, the dissolution rates of enamel and HA were constant, while that of dentine decreased with time. The dissolution rate increased as the pH decreased, and this was most marked for enamel. To compare substrates, the rate of mineral dissolution was normalized to the area occupied by mineral at the specimen surface. For a given acid concentration, the normalized dissolution rate of HA was always less than that for either dentine or enamel. The dissolution rate for dentine mineral was similar to that for enamel at pH 2.45 and greater at pH 3.2 and pH 3.9. The concentration of acid significantly affected the enamel dissolution rate at pH 2.45 and pH 3.2, but not at pH 3.9, and did not significantly affect the dissolution rates of dentine or HA at any pH. The variation in response of the dissolution rate to acid concentration/buffer capacity with respect to pH and tissue type might complicate attempts to predict erosive potential from solution composition.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.650
Threshold uncertainty score0.189

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 head0.013
GPT teacher head0.262
Teacher spread0.249 · 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

Citations104
Published2010
Admission routes1
Has abstractyes

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