Evidence map›Paper›PMID 36143559›Full record

ArticleMaterials (Basel, Switzerland)2022

Investigation of Multicomponent Fluoridated Borate Glasses through a Design of Mixtures Approach.

Kathleen MacDonald, Daniel Boyd

Open access · goldAbstract read
In one paragraph

Article in Materials (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 39% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 7 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Kathleen MacDonaldIR Scientific Inc., Halifax, NS B3H 0A8, Canada.
Daniel BoydIR Scientific Inc., Halifax, NS B3H 0A8, Canada.
Dalhousie University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to their enhanced dissolution, solubility and reaction speed, borate glasses offer potential advantages for the design and development of therapeutic ion-release systems. However, the field remains poorly understood relative to traditional phosphosilicate and silicate bioglasses. The increased structural complexity and relative lack of published data relating to borates, particularly borofluorates, also decreases the accuracy of artificial intelligence models, which are used to predict glass properties. To develop predictive models for borofluorate networks, this paper uses a design of mixtures approach for rapid screening of composition-property relationships, including the development of polynomial equations that comprehensively establish the predictive capabilities for glass transition, density, mass loss and fluoride release. A broad range of glass compositions, extending through the boron anomaly range, were investigated, with the inclusion of 45 to 95 mol% B

Indexed as

bioactive glassborate glassdentistrydesign of mixturesfluoroborateshalogenion release

Identifiers

PMID36143559
PMCPMC9504752
OpenAlexW4296138180

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.