Tooth enamel ultrastructure: correlation between composition and physical properties
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".