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Record W4224264152 · doi:10.11159/icnnfc22.170

In Silico Characterization of Nanomaterials

2022· article· en· W4224264152 on OpenAlexvenueno aff
Anais Colibaba, Konstantinos Τ. Kotsis, Vladimir Lobaskin

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2022
Typearticle
Languageen
FieldEngineering
TopicNanotechnology research and applications
Canadian institutionsnot available
Fundersnot available
KeywordsCharacterization (materials science)In silicoNanomaterialsComputer scienceNanotechnologyMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Nanomaterials (NMs) and nanoparticles (NPs) lie at the core of many technological applications in medicine and pharmacology, as well as in the food, agriculture, electronics, and energy industries.They are also released in the environment through natural and incidental pathways.Despite our heavy reliance on NMs, the potential risk they pose to the environment and to the biological systems is still of major concern [1].In this work, we evaluate intrinsic and extrinsic NM descriptors to aid in the prediction of biomolecular interactions at the surface of NMs and development of structure-activity relationships between their physicochemical characteristics and their toxicity [2].Intrinsic properties are solely based on the molecular and electronic structure of the NM, while the extrinsic properties describe a NM that comes in contact with a protein in a solvent.The NM models for the calculation of intrinsic descriptors are associated with the core of the NM that is described as a periodic bulk material [2].In this work, we present a database of calculated descriptors for several common NMs.The provided list contained various samples of NP (metals, oxides, minerals, polymers, and carbon-based compounds such as carbon nanotubes (CNTs) and graphene sheets) that were used in toxicological experiments.The intrinsic descriptors are evaluated by different theoretical approaches.The bandgap values of all bulk NMs were calculated using density functional theory (DFT) implemented in the SIESTA package [3].The density of states obtained from the DFT calculation at the generalized gradient approximation is used to estimate the size of the bandgap.The heat of formation, absolute hardness, electronegativity, dispersion energy, the dipole moment, the Mulliken electronegativity, the Parr and Pople absolute hardness, and polarizability descriptors were calculated using the semi-empirical PM6-D3 [4] with the MOPAC package [5].The extrinsic (interfacial) descriptor is a quantity that describes the relative hydrophilicity/ hydrophobicity of a periodic surface NM slab (e.g.metals, metal oxides, and carbon-based NMs) in water and octanol based on the relative enthalpy of immersion.In this work, we also employ molecular dynamics with the GROMACS [6] package using e.g.CHARMM force fields [7] in order to obtain the enthalpies of the NM slab, of the solvent (water or octanol) and of the solvated slab metals, metal oxides, and carbon-based compounds.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.245
Teacher spread0.238 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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