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Record W4242390222 · doi:10.1063/1.4922926.1

10.1063/1.4922926.1

2015· dataset· en· W4242390222 on OpenAlexaff
Mu‐Jie Huang, Raymond Kapral

Bibliographic record

VenueDefault Digital Object Group · 2015
Typedataset
Languageen
FieldPhysics and Astronomy
TopicMicro and Nano Robotics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProtein filamentMolecular motorBiological systemPolymerChemistryNanotechnologyChemical physicsMaterials scienceComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Most biological motors in the cell execute chemically powered conformational changes as they walk on biopolymer filaments in order to carry out directed transport functions. Synthetic motors that operate in a similar manner are being studied since they have the potential to perform similar tasks in a variety of applications. In this paper, a synthetic nanomotor that moves along a filament track, without invoking motor conformational changes, is constructed and its properties are studied in detail. The motor is an oligomer comprising three linked beads with specific binding properties. The filament track is a stiff polymer chain, also described by a linear chain of linked coarse-grained molecular groups modeled as beads. Reactions on the filament that are catalyzed by a motor bead and use fuel in the environment, in conjunction within the binding affinities of the motor beads to the filament beads, lead to directed motion. The system operates out of equilibrium due to the state of the filament and supply of fuel. The motor, filament, and surrounding medium are all described at microscopic level that permits a full analysis of the motor motion. A stochastic model that captures the main trends seen in the simulations is also presented. The results of this study point to some of the key features that could be used to construct nanomotors that undergo biased walks powered by chemical reactions on filaments.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.024

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 head0.011
GPT teacher head0.243
Teacher spread0.232 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2015
Admission routes1
Has abstractyes

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