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Record W2289521663 · doi:10.1021/acsnano.5b03367

Controlling Motion at the Nanoscale: Rise of the Molecular Machines

2015· review· en· W2289521663 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Nano · 2015
Typereview
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsMcGill University
FundersGovernment of CanadaU.S. Department of Energy
KeywordsMolecular machineNanotechnologyVariety (cybernetics)Computer scienceIntermolecular forceNanoscopic scaleWork (physics)Motion (physics)Materials scienceArtificial intelligencePhysicsMechanical engineeringMoleculeEngineering

Abstract

fetched live from OpenAlex

As our understanding and control of intra- and intermolecular interactions evolve, ever more complex molecular systems are synthesized and assembled that are capable of performing work or completing sophisticated tasks at the molecular scale. Commonly referred to as molecular machines, these dynamic systems comprise an astonishingly diverse class of motifs and are designed to respond to a plethora of actuation stimuli. In this Review, we outline the conditions that distinguish simple switches and rotors from machines and draw from a variety of fields to highlight some of the most exciting recent examples of opportunities for driven molecular mechanics. Emphasis is placed on the need for controllable and hierarchical assembly of these molecular components to display measurable effects at the micro-, meso-, and macroscales. As in Nature, this strategy will lead to dramatic amplification of the work performed via the collective action of many machines organized in linear chains, on functionalized surfaces, or in three-dimensional assemblies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score0.882

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.283
Teacher spread0.258 · 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