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Record W4386743873 · doi:10.26434/chemrxiv-2023-fd6l9

STEM SerialED: achieving high-resolution data for ab initio structure determination of beam-sensitive nanocrystalline materials

2023· preprint· en· W4386743873 on OpenAlexaff
Pascal Hogan-Lamarre, Yi Luo, Robert Bücker, R. J. Dwayne Miller, Xiaodong Zou

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Electron Microscopy Techniques and Applications
Canadian institutionsUniversity of Toronto
FundersStockholms UniversitetVetenskapsrådetKnut och Alice Wallenbergs Stiftelse
KeywordsWorkflowPython (programming language)Scripting languageSoftwareComputer scienceScanning transmission electron microscopyDiffractionAb initioSnapshot (computer storage)Computational scienceMaterials scienceOpticsCrystallographyTransmission electron microscopyPhysicsNanotechnologyChemistryOperating systemDatabase

Abstract

fetched live from OpenAlex

Serial electron diffraction (SerialED), which applies a snapshot data acquisition strategy on each crystal, was introduced to tackle the problem of radiation damage in the structure determination of beam-sensitive materials by three-dimensional electron diffraction (3D ED). The snapshot data acquisition in SerialED can be realized both in transmission and scanning transmission electron microscopes (TEM/STEM). However, the current SerialED workflow based on STEM setups requires special external devices and software, which brings challenges for its broader adoption. Here, we present a simplified experimental implementation of STEM-based SerialED on Thermo Fisher Scientific STEMs using common proprietary software interfaced through Python scripts to automate data collection. Specifically, we utilize TEM Imaging and Analysis (TIA) scripting and TEM scripting to access the STEM functionalities of the microscope, and DigitalMicrograph (DM) scripting to control the camera for snapshot data acquisition. Data analysis adapts the existing workflow using the software CrystFEL developed for serial X-ray crystallography. Our workflow for SerialED can be used on any Gatan or Thermo Fisher Scientific camera. We apply this workflow to collect high-resolution STEM SerialED data from two aluminosilicate zeolites, Zeolite Y and ZSM-25, and demonstrate, for the first time, ab initio structure determination through direct methods using the STEM SerialED data. Zeolite Y is relatively stable under the electron beam, and SerialED data extend to 0.60 Å. We show that the structural model obtained using SerialED data merged from 358 crystals is nearly identical to that using continuous rotation electron diffraction (cRED) data from one crystal. This demonstrates that accurate structures can be obtained from SerialED. Zeolite ZSM-25 is very beam-sensitive and has a complex structure. We show that SerialED greatly improves data resolution of ZSM-25, compared to serial rotation electron diffraction (SerialRED), from 1.50 Å to 0.90 Å. This allows for the first time the use of standard phasing methods such as direct methods for ab initio structure determination of ZSM-25.

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)
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 score0.246
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.001
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 head0.031
GPT teacher head0.333
Teacher spread0.302 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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