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Record W2088363017 · doi:10.1021/ie030354+

Reactive Crystallization of Brushite under Steady State and Transient Conditions:  Modeling and Experiment

2003· article· en· W2088363017 on OpenAlexaff
A. Tadayyon, S. M. Arifuzzaman, Sohrab Rohani

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2003
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsWestern University
Fundersnot available
KeywordsBrushiteCrystallizationTransient (computer programming)Steady state (chemistry)ThermodynamicsChemistryMaterials scienceChemical engineeringComputer sciencePhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

In recent years, there has been a significant effort among researchers to develop calcium orthophosphate based biomaterials and biomedical devices for prosthetic applications and dental implants. Although the properties of these calcium orthophosphates are quite well-known, very little information is available on the growth and nucleation kinetics of these substances. In the present study, the reactive crystallization of calcium phosphate was studied in a batch system at 25 °C. The initial calcium and phosphorus concentration in the reactor was varied from 0.01 to 0.04 mol/dm 3, and the solution pH was kept under 6. Analysis by XRD, FTIR, atomic absorption and UV spectroscopy revealed that the crystals precipitated from the solution were pure brushite (dicalcium phosphate dihydrate). From the experimental determination of crystal size distribution, and with the aid of a rigorous mathematical model of the process in conjunction with a nonlinear optimization program, nucleation and two-dimensional growth kinetics of brushite crystals were determined. The existence of the two-dimensional growth rate was supported by the plate shape morphology of the growing crystals studied by scanning electron microscopy (SEM).

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.088
GPT teacher head0.327
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 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

Citations9
Published2003
Admission routes1
Has abstractyes

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