MétaCan
Menu
Back to cohort

A Molecular Tool for Carbon Transfer in Mechanosynthesis

2007· article· en· W2095267494 on OpenAlexaff
K. Eric Drexler

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2007
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsNanoXplore (Canada)
Fundersnot available
KeywordsMaterials scienceDimerDelocalized electronEnergy transferMechanosynthesisDiamondRing (chemistry)GrapheneCarbon fibersFullereneNanotechnologyPhotochemistryChemical physicsComposite numberOrganic chemistryComposite materialChemistryBall mill

Abstract

fetched live from OpenAlex

Proposed advanced mechanosynthetic systems [1] require molecular tools able to bind and transfer reactive moieties with high reliability at 300 K (failure rates << 10–10 per transfer operation). Screening of a large number of candidate tools at the AM1 level enabled the identification of a structure, DC10c, that is calculated (at the B3LYP/6- 31G(d,p) level) to meet these stringent requirements when used to transfer carbon dimers to any of a target class of graphene-, nanotube-, and diamond-like structures [2]. The favorable energy of transfer (exoergic by a mean energy ≥ 0.261 aJ per dimer) results from avoidance of the generation of high-energy radical sites during dimer release by means of π-delocalization to form a strained aromatic ring on the binding face of the empty structure. These energies are compatible with transfer-failure rates ~ 10–24 per operation at 300 K, and overall failure rates << 10–10.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.028
GPT teacher head0.298
Teacher spread0.270 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicCarbon Nanotubes in CompositesFrench-language works237,207