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Record W2889400658

The Electronic and Magnetic Effects of 3d Transition Metal Impurities in Semiconductors

2018· dissertation· en· W2889400658 on OpenAlexaboutno aff
Brett Leedahl

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsImpurityCondensed matter physicsSemiconductorMaterials scienceTransition metalMagnetic semiconductorChemistryPhysicsOptoelectronicsQuantum mechanics
DOInot available

Abstract

fetched live from OpenAlex

The subject of this dissertation is concerned almost exclusively with soft x-ray spectroscopy of the 3d transition metals. These relatively common metals, owing to their widespread availability, are already used in all facets of technology. Stainless steel is largely chromium, iron, and nickel; copper wires transmit nearly all of our electricity; nickel is used in the making of margarine. Their wide range of electronic properties, strength, and usefulness in chemical reactions underpins their versatility to the point where they are used in essentially everything we manufacture. Key to this thesis is their ability to induce magnetism. If realized, spintronic technologies would harness the semiconducting electronic properties of a material, while also utilizing the induced magnetic properties to transport spin polarized charge. The ability to advance digital logic (0s and 1s) from its current state as on/off switches controlled solely via current, to the spintronic method of flipping spins to an up or down state, would have vast consequences for the computing world. Heat dissipation and cooling issues would largely vanish, and computing speed would show large improvements, while being non-volatile when power is lost.\n\nSuccinctly put, the broad goal of my studies focused on how transition metal impurities doped into host semiconductors in small proportions can influence the host material's electronic and magnetic properties. This was accomplished primarily through modelling experimental spectra with theoretical calculations, and then extracting information through their agreement. In doing this, it is possible to determine fundamental quantitative properties of for each 3d ion, and how each ion situates itself within the host lattice. This information can then be linked back to known properties of the material in order to determine which 3d ions, host materials, and synthesis conditions show promise for spintronic or other related technologies.\n\nFor the study concerning Bi2Te3 it was shown that the various transition metals Cr, Mn, Fe, Co, Ni, and Cu each integrate themselves into the host crystal in a particular fashion. Manganese atoms substitute cleanly into Bi sites; chromium atoms are not absorbed into the bulk, but only the surface; iron prefers a mixture of oxidation states; and for cobalt and nickel a mixture of configurations was found. Similarly, with host materials TiO2 and ZnO, DFT calculations predicted that the probability of substitution by a transition metal atom into a Zn or Ti site decreased in probability as the atomic number of the dopant metal atom increases, with a greater chance of metallic clustering in TiO2. Spectroscopic measurements, along with crystal field calculations confirmed these trends though modelling and direct comparison of calculation and experiment. This allowed us to extract real physical properties of the system, such as oxidation state, local symmetry, and effects d-orbital energies, via the calculation parameters. \n\nIn the ferromagnetic compound NiFe2O4, the Fe atoms are responsible for the magnetism, but are in three different unique sites of various oxidation states and symmetries. By theoretically modelling x-ray magnetic circular dichroism experiments I have shown how these three sites can be readily distinguished, and how the interplay between their individual contributions to the magnetism are necessary to understand how the bulk magnetism arises. Furthermore, only through modelling the experimental XMCD with calculations can it be understood how aluminum alloying affects the overall magnetism. As more non-magnetic aluminum atoms replace magnetic iron atoms, the overall strength of the magnetism does not continuously decrease, but in fact begins to increase again at a certain point; this unexpected and unintuitive result can only be explained using the methodology described above.\n\nStructural changes in regular white TiO2 occur under a high pressure atmosphere that cause it to turn black, as a result of mid band gap states forming. I was able to adapt a generally hard x-ray technique (EXAFS) to the soft x-ray regime using the capabilities of the REIXS beamline at the Canadian Light Source to probe the change in interatomic distances between the white and black materials and observe the undergone structural changes. The shift in atomic distances were then compared to distorted structures of the nominal material and a distortion in the vicinity of an oxygen vacancy were able to solve the dilemma of the nature of the distortion. \n\nThe Chelyabinsk meteorite had a thermomagnetic analysis performed on it to determine the various Curie temperatures of the magnetic materials contained in it, which consists of nickel and iron. Through comparisons with magnetic phase charts, we showed that the meteorite contains an iron-nickel alloy, which is quite common. But the breakthrough finding that had not been observed before was the discovery of an extremely pure form of iron, which hadn't ever been observed to occur naturally before.

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 score0.003

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 communication0.0010.000
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 head0.002
GPT teacher head0.204
Teacher spread0.202 · 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
Published2018
Admission routes1
Has abstractyes

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