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Record W7132964159

Terahertz and Raman Characterization of Gas Phase Synthesized Indium Nitride Nanostructures

2023· dissertation· W7132964159 on OpenAlexaff
Rajiv Prinja

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldPhysics and Astronomy
TopicGaN-based semiconductor devices and materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRaman spectroscopyIndium nitrideIndiumNanostructurePhase (matter)Characterization (materials science)HydrogenSputteringDensity functional theory
DOInot available

Abstract

fetched live from OpenAlex

Nanoparticles are of immense importance in diverse fields of fundamental and applied research. However, not all the materials can be effectively synthesized via solution-based chemistry techniques. In contrast, fabrication of nanostructures using ultra-high vacuum-based gas phase synthesis method opens a new paradigm in the synthesis of advanced functional nanomaterials. In the present work, we demonstrate the feasibility of synthesizing indium nitride nanostructures using the gas phase cluster agglomeration method. We show that indium nitride nanostructures having a range of sizes, morphologies and structures can be synthesized using one, direct reactive (argon and nitrogen) sputtering of indium, and two, direct gas-phase synthesis where indium directly reacts with molecular nitrogen; in both cases the clusters coalesce in the aggregation zone albeit with marked differences in the chemistry at play and hence the resulting structural differences. These differences are further accentuated upon introduction of molecular hydrogen in the growth dynamics. Indium nitride nanostructures are characterized using scanning and transmission electron microscopy techniques. The nanostructures are further characterized using Raman spectroscopy and terahertz spectroscopy. The experimental results are subsequently analyzed in relation to density functional theory computational modelling. First analysis shows very good correlation between experimentally observed and theoretically calculated vibrational modes of the nanostructures; the Raman data reveal the important role of surface modes. Correlation between the observed properties and the deposition/synthesis parameter space is clearly established. Furthermore, relative ratios of molecular nitrogen to molecular hydrogen in the growth region show a marked effect on their vibrational spectra, thereby opening a new approach to tuning various Raman modes in a material during the growth process itself. Terahertz spectroscopy reveals the effect of environmental exposure on the as grown nanostructures, in particular, on the surface composition of the indium nitride. THz spectroscopy also yields the dielectric properties of the indium nitride core.

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 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 score0.010
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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.310
Teacher spread0.295 · 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.

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
Published2023
Admission routes1
Has abstractyes

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