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Record W2067167794 · doi:10.1021/jp404311a

Understanding the Surfaces of Nanodiamonds

2013· article· en· W2067167794 on OpenAlexaff
Jeffrey T. Paci, Han Man, Biswajit Saha, Dean Ho, George C. Schatz

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2013
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsUniversity of Victoria
FundersNational Science Foundation
KeywordsSulfuric acidNanodiamondPhenolsZeta potentialChemistryParticle sizeSulfonic acidParticle (ecology)TitrationGraphiteInorganic chemistryPhenolOrganic chemistryChemical engineeringMaterials scienceNanoparticleNanotechnologyPhysical chemistry

Abstract

fetched live from OpenAlex

Functional groups and their associated charges are responsible for the binding and release of molecules from the surfaces of particles in nanodiamond colloids. In this work, we describe a combined set of experimental and computational techniques that are used to characterize these functional groups quantitatively. The surfaces of the particles examined during this study are amphoteric, as one would expect for surfaces made of carbon, with high concentrations of phenols, pyrones, and sulfonic acid groups; the average 50-nm-diameter nanodiamond aggregate has approximately 22000 phenols, 7000 pyrones, and 9000 sulfonic acids. The aggregates also have at least 2000 fixed positive charges, stabilized within pyrones and/or chromenes. No evidence for a significant concentration of carboxylic acid groups was found, although some are probably present. Hydroxyl and epoxide groups are present on some areas of the surfaces. The surfaces are graphitized, so the presence of phenols and pyrones is not surprising because such groups are common on graphitic surfaces. The sulfonic acid is due to the sulfuric acid treatment used to remove amorphous carbon and graphite during particle cleaning. The fixed charges are also due to the cleaning procedure that includes the use of KMnO 4 with the sulfuric acid. Based on titration and zeta potential experiments, elemental and particle size analyses, and modeling using semiempirical quantum mechanics, a model is proposed for the types and concentrations of surface groups. The modeling shows how functional groups form during the bead milling and cleaning used in the preparation of the colloid. It also shows that the p K a associated with the phenols and pyrones that are formed (p K a = 7.6–10.0) is consistent with that predicted using titration experiments (p K a ≥ 7.3). The positive surface potential means that the latter p K a value is significantly larger than a Henderson–Hasselbalch-based estimate. The model is shown to be useful in explaining a number of recent experiments in which nanodiamonds were used to bind and release therapeutic drug and polymer molecules.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.039
GPT teacher head0.265
Teacher spread0.226 · 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

Citations87
Published2013
Admission routes1
Has abstractyes

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Same venueThe Journal of Physical Chemistry CSame topicDiamond and Carbon-based Materials ResearchFrench-language works237,207