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Record W2980685644 · doi:10.1063/1.5108512

Modular thermal Hall effect measurement setup for fast-turnaround screening of materials over wide temperature range using capacitive thermometry

2019· article· en· W2980685644 on OpenAlexaff
Ha-Leem Kim, Matthew J. Coak, Jordan Baglo, Keiron Murphy, R. W. Hill, Michael Sutherland, Monica Ciomaga Hatnean, G. Balakrishnan, Je‐Geun Park

Bibliographic record

VenueReview of Scientific Instruments · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of Waterloo
FundersEngineering and Physical Sciences Research CouncilInstitute for Basic Science
KeywordsCryostatShielded cableModular designMaterials scienceCoaxialHall effect sensorTemperature controlNuclear engineeringOptoelectronicsComputer scienceMechanical engineeringPhysicsMagnetEngineeringTelecommunicationsSuperconductivity

Abstract

fetched live from OpenAlex

We demonstrate a simple and easy-to-build probe designed to be loaded into a widely available Quantum Design Physical Properties Measurement System (PPMS) cryostat, with a detachable shielded sample puck section and robust heat sinking of three pairs of coaxial cables. It can be in principle used with any low-temperature cryostat. Our modular puck design has a radiation shield for thermal isolation and protection of the delicate sample space while handling and allows any variety of experimental setup benefiting from shielded coaxial wiring to be constructed on a selection of sample pucks. Pucks can be quickly and easily switched, and the system makes use of the simple yet extremely stable temperature and magnetic field control of the easy-to-use PPMS system. We focus on a setup designed for measurements of the thermal Hall effect and show that this system can yield unprecedented resolution over a wide temperature range and with rapid sample mounting or changing—allowing a large collection of potential samples to be screened for this novel physics. Our design aims to make these sensitive but challenging measurements quick, reliable, cheap, and accessible, through the use of a standard, widespread base cryostat and a system of modular removable sample stage pucks to allow quick turnaround and screening of a large number of candidate samples for potential new thermal Hall physics.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.822

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.025
GPT teacher head0.261
Teacher spread0.236 · 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

Citations11
Published2019
Admission routes1
Has abstractyes

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