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Record W4401001056 · doi:10.1093/mam/ozae044.981

The Characterization of Newly Secreted Dental Enamel by Electron Energy Loss Spectroscopy

2024· article· en· W4401001056 on OpenAlexaff
Ya-Hsiang Hsu, Asra Hassan, Amanda H. Trout, John D. Bartlett, Charles E. Smith, David W. McComb

Bibliographic record

VenueMicroscopy and Microanalysis · 2024
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsMcGill University
Fundersnot available
KeywordsCharacterization (materials science)Electron energy loss spectroscopyEnamel paintMaterials scienceDental enamelSpectroscopyEnergy-dispersive X-ray spectroscopyAnalytical Chemistry (journal)ChemistryNanotechnologyScanning electron microscopeComposite materialPhysicsChromatographyTransmission electron microscopy

Abstract

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Teeth are one of the hardest organs in the human body. Typically, mature enamel contains up to 95% of inorganic minerals and 5% of organic components, with hydroxyapatite (HA, Ca10(PO4)6(OH)2) constituting the majority of the inorganic material [1]. However, during the development of the tooth, the forming HA phase in enamel lacks a high degree of crystallization, leading to cheesy texture and weak mechanical properties until the maturation process is completed. The abundance of protein in developing enamel also makes it sensitive to the electron beam, posing a challenge in characterizing evolving changes in crystallization. Moreover, in 2016, X-ray diffraction (XRD) analysis revealed the presence of octacalcium phosphate (OCP, Ca8H2(PO4)6) in the enamel of the amelogenin knockout mice [2]. In 2018, Yamazaki et al. detected a strong signal of OCP in the matrix metalloproteinase-20 (MMP20) knockout teeth using Raman microspectroscopy [3]. Lately, our previous research observed a large portion of OCP fan-shaped enamel crystals in the MMP20 knockout teeth with the characterization of the selected area diffraction pattern (SADP) [4]. Due to the similar composition and crystal structure, distinguishing between HA and OCP is difficult. While XRD and SADP allow identification of the crystal structure, they lack elemental and composition information. Raman microspectroscopy offer bonding information but lacks spatial resolution. Hence, a more powerful technique is needed for the characterization of abnormal secretory teeth. In this research, the analysis of electron energy loss spectroscopy (EELS) was presented to provide the elemental and bonding information and coupled with excellent spatial resolution for elemental mapping. Pure HA and OCP particles were first accessed as reference for EELS analysis. When observed under the transmission electron microscope (TEM), HA and OCP particles showed distinguishable shapes (Fig. 1A). The HA particles appear as small spherical clusters, and the OCP particles appear as large plane crystals. Fig. 1B, 1C, and 1D are the phosphorus L2,3-edge, calcium L2,3-edge, and oxygen K-edge EEL spectra respectively. These spectra are high-loss spectra, and they are associated with electron transitions between the orbitals of each element. For the phosphorus L2,3-edge, both spectra exhibited similar shapes. This similarity suggests that the bonding of phosphorus in both HA and OCP is alike. This result is consistent with the phosphorus configurations in HA and OCP as they both originate from the phosphate groups (PO43-). Similarly, the calcium L2,3-edge of HA and OCP were alike because all the calcium signals originated from Ca2+. In contrast, the oxygen K-edge from HA and OCP showed a slight difference. Two distinct peaks could be observed at 537 and 540 eV in the HA spectrum but less noticeable in the OCP spectrum. Because this difference was not very significant, an EELS simulation was conducted with FEFF software (Fig. 1E). The HA simulation revealed a two-peak feature, whereas the OCP simulation displayed only one peak. This simulation is consistent with the experimental results. This minor variation can be attributed to the hydroxide group (OH-) in HA [5]. The elemental quantification was carried out with a spatial resolution of 2 nm per pixel (Fig. 2). Mapping of the phosphorus and calcium signals not only facilitates the outline of the crystal shape clearly but also helps to distinguish between HA and OCP. In the overlay image (Fig. 2C), the color of the plane crystal appears redder than that of the spherical particles, implying different Ca/P ratios of the two crystalline phases. In addition, the plot of intensity versus distance (Fig. 2D) revealed similar intensity of the phosphorus and calcium signal in the HA particles, but the phosphorus intensity was much higher than the calcium in the OCP crystal. This plot assists in classifying HA and OCP while also indicating their boundaries. Aside from the mapping, because of the known Ca/P ratios of HA and OC, an experimental factor could be calculated for the use of relative quantification in further analysis. Two sites of forming enamel in wild-type mouse incisors were observed with EELS in this research. Fig. 3A and 3B shows the TEM images and the elemental mapping from the region of the dentin-enamel junction (DEJ). Comparing the two images, it was observed that the dark regions in the TEM image display a higher carbon signal and lack of crystals which are made of calcium and phosphorus. The fiber structure in the TEM images were clearly delineated in the mapping image. With the experimental factor calculated from the reference samples, the Ca/P ratio of the fibers and dentin was around 1.68, which is close to the Ca/P ratio of HA. Surprisingly, besides the fibers, certain regions (labeled stars in Fig. 3B) also displayed high calcium and phosphorus signals, and those regions contained higher carbon signals than the fibers. Fig. 3C and 3D represent another EELS acquisition that includes enamel crystals and space of Weber (SW). SW is a distinctive area usually lacking mineral, emerging during the development of enamel in rodent incisors. Fig. 3D illustrates low calcium and phosphorus intensity in the SW area. With this overlaid image, we can visibly observe the microstructure of the enamel crystal and note that there were strong carbon signals between enamel crystals. Fig. 3E revealed the O K-edge spectrum from the reference samples and enamel crystals. A small peak around 536 eV was observed in the enamel spectrum, suggesting that the early enamel crystals are more similar to HA than OCP. EELS stands out as a potential technique for material characterization, offering not only bonding and elemental information but also excellent spatial resolution for elemental mapping and delineation of material boundaries. In this study, the HA and OCP reference samples were characterized and differentiated with EELS analysis. The elemental mapping outlined the shape of the crystals and revealed different Ca/P ratios. With the established analysis method, a wild-type tooth sample was examined. With the EELS elemental mapping, the enamel crystals and enamel matrix were distinctly identified. Relative quantification and the feature of O K-edge implied immature enamel crystals were more similar to HA instead of OCP. In the future, further investigation on the MMP20 knockout tooth will be observed and analyzed with the same method to figure out more information about tooth development. (A) Scanning transmission electron microscopy (STEM) image of HA and OCP crystal. (B) Phosphorus L2,3-edge, (C) Calcium L2,3-edge, and (D) Oxygen K-edge EEL spectra of HA and OCP. (E) EELS simulation for Oxygen K-edge of HA and OCP with FEFF software. Elemental mapping with (A) Phosphorus L2,3-edge and (B) Calcium L2,3-edge EELS signal. (C) The overlay image of phosphorus L2,3-edge and calcium L2,3-edge signal. (D) The x-axis projection of phosphorus and calcium signal. The STEM image and elemental mapping (A, B) in the region of DEJ, and (C, D) in the region enamel layer including the space of Weber from the wild-type mouse incisor. (E) The O K-edge spectra of HA, OCP, and enamel crystals. SW, space of Weber.

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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 score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.242
Teacher spread0.239 · 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".

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Published2024
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