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Record W3190166310 · doi:10.1021/acsnano.1c00209

Sub-4 nm Nanodiamonds from Graphene-Oxide and Nitrated Polycyclic Aromatic Hydrocarbons at 423 K

2021· article· en· W3190166310 on OpenAlexaff
Yuting Shen, Shi Su, Wen Zhao, Shaobo Cheng, Tao Xu, Kuibo Yin, Linjiang Chen, Longbing He, Yilong Zhou, Hengchang Bi, Shu Wan, Qiubo Zhang, Liang Wang, Zhenhua Ni, Florian Banhart, Gianluigi A. Botton, Feng Ding, Rodney S. Ruoff, Litao Sun

Bibliographic record

VenueACS Nano · 2021
Typearticle
Languageen
FieldMaterials Science
TopicDiamond and Carbon-based Materials Research
Canadian institutionsMcMaster University
FundersInstitute for Basic ScienceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsGraphenePhenanthreneAnthraceneNaphthalenePyreneOxideTriphenyleneCarbon fibersNanoscopic scaleCoroneneNanotechnologyMaterials scienceChemistryChemical engineeringOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Nanodiamonds are interesting materials from the point of view of their biocompatibility and their chemical, spectroscopic, and mechanical properties. Current synthetic methods for nanodiamonds involve harsh environments, which are potentially hazardous in addition to being expensive. We report a low-temperature (423 K) hydrothermal approach to form nanodiamonds by using graphene-oxide or nitrated polycyclic aromatic hydrocarbons (naphthalene, anthracene, phenanthrene, or pyrene) as a starting material. The reaction products contain single-crystalline or twinned nanodiamonds with average diameters in the 2–3 nm range. Theoretical calculations prove that, at the nanoscale, sub-4 nm nanodiamonds may adopt a structure that is more stable than graphene-oxide and nitrated polycyclic aromatic hydrocarbons. Our findings show that sp2 carbon in the polycyclic aromatic precursor can be converted to sp3 carbon under unexpectedly moderate temperature conditions by using nanoscale precursors and thus offer a low-temperature approach for the synthesis of sub-4 nm nanodiamonds.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.007
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 integrity0.0000.000
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 head0.012
GPT teacher head0.234
Teacher spread0.223 · 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.

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

Citations19
Published2021
Admission routes1
Has abstractyes

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