Fabrication and Characterization of Materials for Ultrafast Electron Scattering Experiments
Bibliographic record
Abstract
Ultrafast Electron Scattering (UES) as a technique has unique capabilities in terms of interrogating the fundamental processes that underly the novel properties of 2D-layered and strongly-correlated materials with subpicosecond temporal resolution.Despite its potential to deliver important new insights into such materials, the implementation is hampered by an intricate issue of its specific sample requirements.The sample must be macroscopic in lateral dimensions (100 microns) and thin (10s of nm) enough to allow electrons to penetrate and interact with the material's structure.In this thesis, a novel approach is adopted, modified, and implemented to achieve high-quality throughput samples for UES experiments.This approach involves exfoliating layered materials via metal Gold(Au)-assisted tape.This method prove particularly effective on materials like WS 2 , SnS 2 , Graphite and high-temperature superconductor Bi-2212 (Bi 2 Sr 2 CaCu 2 O 8+x ), which resulted in producing laterally extended ( 200m), and ultra-thin (< 5 nm) single crystals flakes.In addition, Transmission Electron Microscopy(TEM), Raman spectroscopy, and UES techniques were used to qualitatively characterize the nature of these materials.Intrinsically 3D materials can also be prepared for UES experiments via thin film deposition approaches.Previously, the Siwick group had investigated VO 2 films prepared by PLD (Pulsed Laser Deposition) technique.However, this time we encountered many notable problems with PLD grown VO 2 thin film samples.To address those issues, we systematically investigated the quality and phase of VO 2 thin films.Our diagnostics reported the presence of an unusual B-phase, which did not align with our inclination to research on VO 2 M1-phase in UES trials.After adjusting the PLD parameters, we received a fresh batch of VO 2 samples that displayed the desired M1 to i R (rutile) Phase transition on 56 o C. Upon addressing the concerns, we are prepared to start the novel Debye-Waller experiments soon.As I pause on this significant threshold of my journey to reflect on the myriad of individuals who have enriched my path, my heart is filled with profound gratitude.To Dr. Bradley J. Siwick, a guiding light in the world of science and an unwavering pillar of support throughout my MSc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".