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Record W2950300313 · doi:10.82308/591

Tooth enamel ultrastructure: correlation between composition and physical properties

2012· article· en· W2950300313 on OpenAlexaboutno aff
Hazem Eimar

Bibliographic record

VenueeScholarship@McGill (McGill) · 2012
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsEnamel paintTooth enamelHueLightnessMaterials scienceHuman toothDentistryMineralogyChemistryComposite materialOpticsMedicine

Abstract

fetched live from OpenAlex

Tooth enamel is one of the hardest materials found in the animal kingdom. Accordingly, understanding its unique characteristics is of special interest for the interpretation of distinct tooth physical properties and for the development of new synthetic materials. Enamel is a composite material that comprises an inorganic matrix composed of hierarchically organized carbonated hydroxyapatite (HA) crystals, and an organic matrix mainly composed of the protein amelogenin. This study was designed to investigate how variations in enamel ultrastructure and chemical composition (organic and inorganic) may affect tooth mechanical and optical properties.One hundred extracted sound teeth were collected from adult patients attending McGill Undergraduate Dental Clinic. Vickers microindentation, Shade spectrophotometry, FTIR, SEM-EDS and XRD were used to asses enamel microhardness, tooth shade (registered in universal shade parameters: lightness, chroma and hue), chemical composition and crystallography (i.e. crystal size, lattice parameters of the crystal; a-axis and c-axis). The data obtained was analyzed for correlation, and statistical significance was set at P <0.05. Tooth enamel crystallographic structure, chemical composition, optical and mechanical properties varied dramatically within the studied population. Tooth enamel microhardness was affected by the size of its HA crystals (R= -0.476, B= -0.028, P= <0.001). Tooth shade hue was influenced by enamel HA crystal size (R= -0.358, B= -0.866, P= 0.007), tooth shade chroma was influenced by enamel HA carbonization (R= -0.419, B= -99.06, P= <0.001), and tooth shade lightness was influenced by both enamel HA crystal size (R= -0.313, B= -1.052, P= 0.019) and the degree of HA carbonization (R= -0.265, B= -57.95, P= 0.033). On the other hand, tooth enamel HA crystal size was inversely correlated with the organic relative content within tooth enamel (R= -0.352, B= -19.4, P= 0.016).This study highlights the variation in tooth enamel ultrastructure and chemical composition, and their association with its optical and mechanical properties. For instance, we have revealed that the organic content in teeth indirectly regulates enamel mechanical and optical properties by controlling the size of HA crystals. On the other hand, variation in the degree of enamel HA carbonization can also affect the tooth optical properties.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.244
Teacher spread0.221 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

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