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Record W3195500187 · doi:10.21203/rs.3.rs-599265/v1

Computational Analysis and First Principle Study of Electrical and Optical Properties of Fe Doped SWBNNT and its Application

2021· preprint· en· W3195500187 on OpenAlexaff
Abinash Thapa, Bibek Chettri, Arghyadeep Sarkar, Prashant Pradhan, Bikash Sharma

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicBoron and Carbon Nanomaterials Research
Canadian institutionsMcMaster University
FundersAll India Council for Technical Education
KeywordsDopingMaterials scienceEngineering physicsComputer scienceOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Abstract The electrical and optical properties of One Dimension-Single Walled Boron Nitride Nanotube (1D-SWBNNT) doped with transition metal Iron are studied using the Quantum ATK. Highest direct bandgap obtained for S1 as 5.3167eV and S3 as 3.5328eV depicted the possibility of its use as a dielectric in the memory device. SWBNNT showed a consistent bandgap for varying lengths of the NT. Bandgap tunability and a moderate increase in the number of states in Density of States (DOS) plots can be achieved by the inclusion of transition metal dopants in pristine SWBNNT. In Projected-DOS (PDOS) plots we observed N and Fe atoms as the majority contributor of electronic states in the valence band and Fe atom as the main contributor in the conduction band. The inclusion of Fe dopant leads to an increase in the wavelength and optical gap. High optical conductivity for S2, S3, and S4 depicts its use as composites in photoconductive devices. The incorporation of Fe dopant led to a rise in susceptibility ( χ ) where S1 and S2-S4 showed a weak diamagnetic and strong paramagnetic property. BNNTs technologies are still growing, there is a need for further development bringing out its vast applications in the future.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
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 head0.062
GPT teacher head0.379
Teacher spread0.317 · 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 teacher head, 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

Citations2
Published2021
Admission routes1
Has abstractyes

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