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Enhancement of Local Terahertz Electric Field by a Semiconductor Nano-Dumbbell

2024· article· en· W4401723511 on OpenAlexaboutno aff
Zi Wang, Thomas Wong

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsnot available
Fundersnot available
KeywordsTerahertz radiationElectric fieldSemiconductorNano-DumbbellMaterials scienceOptoelectronicsField (mathematics)PhysicsComposite materialQuantum mechanics

Abstract

fetched live from OpenAlex

Semiconductor nanoparticles (SNP) are of current interest for applications in the terahertz frequency range owing to their surface plasmon resonance (SPR) frequency being amenable to be situated in the terahertz range by doping. They can play roles in the terahertz range similar to those of metallic nanoparticles in the optical region. With a lower charge carrier concentration in extrinsic semiconductors, space-charge effects are more prevalent in SNP than in their metallic counterparts. A transport formulation for carrier dynamics is necessary to provide realistic accounts of the charge distribution and the internal field in an SNP polarized by an externally applied terahertz electric field (T. Shen, M. Yan, and T. Wong, “Charge Polarization and Current Distribution in a Conductive Particle in the Rayleigh Region”, IEEE Trans. Antennas and Propagation, v. 61, pp. 4229–4238, Aug. 2013). Clusters of SNP can be formed to intensify the effects of their polarization. A particle pair (dimer) is the simplest form of cluster of fundamental interest to gain insight to interactions between particles in terms of shift in SPR frequency and local field enhancement. The potential of a non-overlapping semiconductor nanodimer (SND) as a scanning sensor in the terahertz range has been studied (Z. Wang and T. Wong, “Field Dynamics in the Gap of a Semiconductor Nanodimer,” Proc. IEEE NANO, 2021, pp. 414–417, Montreal, July, 2021). A surface or substrate is required to anchor the two SNPs to maintain the gap spacing between them for a non-overlapping SND. In a fluidic environment, it is more practical to have a single nanostructure to perform functional tasks such as field enhancement and sensing. For these purposes, the semiconductor nano-dumbbell (SNDB) may be employed. It is actually an SND in the overlapping regime but for better visualization of the geometry, the dumbbell shape is emphasized in its nomenclature. In this paper, field simulation for an SNDB polarized by a terahertz electric field employing a transport formulation to reveal the space-charge interaction is reported. Results indicate that two orders of magnitude in field enhancement at the SPR frequency is accomplished at the waist of the SNDB, as shown in the figure, in which the field surrounding a single SNP of same radius and a semiconductor nanorod with same axial length as that of the SNDB are also displayed for comparison. The strong enhancement action of the SNDB is attributed to the presence of space charge of opposite polarities on two sides of the wedge structure at the waist of the SNDB, while opposite charges on the SNP and on the nanorod are concentrated at locations which are much farther apart from each other.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.001

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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