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A Molecular Tool for Carbon Transfer in Mechanosynthesis

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
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 score0.285
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0040.004
Research integrity0.0000.001
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 head0.028
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