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Record W4412909067 · doi:10.1093/mam/ozaf048.313

Femtosecond Laser-Induced Crystallization of Cytosine Monohydrate for Serial Nano Electron Diffraction Analysis

2025· article· en· W4412909067 on OpenAlexaffabout
Man Sze Cheng, Sreelaja Pulleri Vadhyar, Ehsan Nikbin, Yasuchika Suzuki, Harmanjot Grewal, Manoel L. da Silva-Neto, R. J. Dwayne Miller

Bibliographic record

VenueMicroscopy and Microanalysis · 2025
Typearticle
Languageen
FieldMaterials Science
TopicEnzyme Structure and Function
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFemtosecondMaterials scienceCrystallizationLaserNano-CytosineElectron diffractionDiffractionOpticsCrystallographyChemistryDNAPhysicsOrganic chemistryComposite materialBiochemistry

Abstract

fetched live from OpenAlex

Determining the atomic structure of materials and molecules is essential for understanding their properties and functions in both materials and life sciences.X-ray crystallography has long been the standard technique, but its reliance on large, well-ordered crystals presents challenges for certain materials, such as membrane proteins [1].Microcrystal electron diffraction (MicroED) has proven effective for solving the structures of biomaterials and small molecules [2].However, its reliance on continuous crystal rotation and high-energy electron exposure limits its applicability to beam-sensitive materials.Recently, serial nano-electron diffraction (SerialNED) has been developed for structure determination of small molecules [3] and proteins [4].Adapted from the snapshot data-collection approach used in serial X-ray crystallography, SerialNED offers key advantages over traditional X-ray diffraction, including lower sample requirements, reduced radiation damage, and comparable resolution [5].A major challenge in SerialNED is the preparation of nanocrystals.Here, we present a novel approach that employs a femtosecond (fs) laser to precisely control the crystallization process [6], enabling the rapid formation of nanocrystals for electron diffraction analysis.This method enhances nucleation efficiency, producing single crystals of organic molecules and proteins with minimal material consumption.By streamlining nanocrystal production, it significantly accelerates the structure determination process compared to conventional batch crystallization techniques.One of our target materials is cytosine, a fundamental DNA base.When cytosine dissolves in water, it forms cytosine monohydrate crystals, which are typically challenging to prepare as nanocrystals suitable for transmission electron microscopy (TEM) analysis due to difficulties in microtoming.Cytosine serves as an excellent testing material not only because it is a DNA base but also because it readily crystallizes [7].After preparing the sample solution, a femtosecond laser (Ti:Sapphire, 800 nm, 35 fs, 1kHz) irradiates it on electron microscopy grids, promoting crystal nucleation.The grids are then dried using a suction unit and prepared for TEM analysis.Using laser-induced crystallization, we successfully generated a relatively large quantity of nanometer-thin cytosine monohydrate crystals.The unit cell parameters and morphology of cytosine monohydrate crystals grown using the femtosecond laser match those grown via conventional methods.These crystals exhibited high-quality diffraction patterns, as illustrated in Fig. 1C.We then applied serial nano-electron diffraction (SerialNED) to these samples, determining the structure with sub-angstrom resolution using a 90 kV electron microscope at room temperature.The experiments were carried out using a Hitachi HT7800 transmission and scanning transmission electron microscope (TEM/STEM), coupled with an X-Spectrum Amber 750K detector, to determine the structure of cytosine monohydrate.Electron diffraction patterns were collected sequentially using the Azorus software package (Hitachi High-Tech Canada Inc.) to map the crystal coordinates.Over 10,000 diffraction patterns were obtained from the sample, enabling the structure to be determined with high resolution (Fig. 2).In summary, femtosecond laser-induced crystallization provides a powerful approach to accelerate and control crystallization processes.This technique enables the production of high-quality single crystals suitable for electron diffraction.By combining this method with SerialNED, it represents a high-throughput solution for structural determination across a wide range of materials [9].

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 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 score0.041
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.007
GPT teacher head0.261
Teacher spread0.254 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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