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Enregistrement W4401006433 · doi:10.1093/mam/ozae044.704

Streak Imaging in a Dynamic Transmission Electron Microscope

2024· article· en· W4401006433 sur OpenAlexaff
Kenneth R. Beyerlein, Samik Roy Moulik, Yingming Lai, Aida Amini, Patrick Soucy, Jinyang Liang

Notice bibliographique

RevueMicroscopy and Microanalysis · 2024
Typearticle
Langueen
DomaineMaterials Science
ThématiqueElectron and X-Ray Spectroscopy Techniques
Établissements canadiensInstitut National de la Recherche Scientifique
Organismes subventionnairesnon disponible
Mots-clésStreakStreak cameraElectron microscopeTransmission electron microscopyMaterials scienceScanning transmission electron microscopyConventional transmission electron microscopeOpticsElectron tomographyTransmission (telecommunications)MicroscopeNanotechnologyComputer sciencePhysicsTelecommunications

Résumé

récupéré en direct d'OpenAlex

A suite of high-speed electron microscopy instruments and methods have been developed in the past few decades to watch nanomaterial excitation and dynamics in real time [1-3]. The most widespread is the ultrafast transmission electron microscope (UTEM), which has been used to study light-induced excited states of materials with femtosecond time resolution. As a single pulse does not contain sufficient electrons to form an image, a UTEM image is collected stroboscopically, and capturing one single frame can require an exposure of more than a minute [1]. A time series of 20 frames can then take 30 minutes to collect, requiring stable microscope operation, no sample degradation, and its reversible response on this time scale. Alternatively, the dynamic transmission electron microscope (DTEM) has been developed for snapshot imaging of transient and irreversible processes such as phase transformations. The DTEM relies on the generation of high-charge electron pulses (∼107–108 electrons per pulse) to form an image in a single shot [4,5], while the movie-mode DTEM (MM-DTEM) can film a transient event utilizing a train of such pulses [6]. At this high-charge limit, electron-electron interactions play a significant role in defining many properties of the pulses. For example, the maximum photoemitted current density is governed by the Child-Langmuir limit [7,8], the pulse energy distribution is broadened by the Boersch effect [1], and the pulse also suffers from longitudinal and transverse broadening at beam crossovers as it propagates in the column [1]. Despite all these effects, no measurements of the temporal profile of high-charge electron pulses in a DTEM have been previously reported. This work presents our recent progress in implementing different modes of streak imaging functionality in a MM-DTEM, and its use to characterize the spatio-temporal evolution of the generated high-charge electron pulses. Notably, we will also demonstrate the application of tomographic compressed sensing image reconstruction to recover a sequence of two-dimensional images of a 1.85-µm-diameter field of view (FOV) with nanoscale spatial resolution, 370-ps inter-frame interval, and 140-frame sequence depth in a 50-ns time window. This new functionality has the possibility to rapidly collect sets of images with picosecond time resolution, relieving the constraint on sample stability, and allowing for new in situ experiments that follow the ultrafast material response as it evolves. The movie-mode DTEM at INRS is comprised of an IDES® cathode laser system and a modified JEOL® JEM 2100-Plus TEM [6]. The microscope contains electrostatic beam deflectors positioned downstream of the image forming lenses, which in the movie-mode function, applies a static voltage as the electron pulse is traveling through a pair of metal plates to deflect the image onto different regions of the camera. We realized streak-mode DTEM (SM-DTEM) functionality by installing a custom deflector voltage controller (Axis Photonique®) to apply a voltage ramp to the plates synchronized with the electron pulse. This formed a streak image on the camera that separated the electron arrival time into different positions on the camera along the streak direction. The most straight forward analysis for this kind of measurement consists of extracting a linear trace along the streak direction to obtain the average transmitted intensity at different times during the pulse. We consider this a zero-dimensional (0D) SM-DTEM measurement, as it yields the evolution of the average transmitted electron intensity over a FOV defined by a selected area aperture (SA) and used it to characterize the temporal profile of the photoemitted electron pulse. Figure 1 shows a set of 0D SM-DTEM traces collected as a function of cathode laser pulse energy with the beam centered in a 100-μm SA and a sweep rate of 4 V/ns, corresponding to an estimated time resolution of 7 ns. It is seen that at high pulse energy, there is a peak in the photoemission at the beginning of the electron pulse. As the pulse energy is decreased, this peak gradually shifts from 18 ns to 10 ns. However, it is found to persist to a UV pulse energy of 0.03 mJ, well below the photoemission saturation limit. We will present this result cross-referenced with other complementary measurements, to show that this peak in the photoemission profile does not follow the cathode laser pulse and is believed to be caused by anomalies in the photoemission process. Then, we will introduce two-dimensional (2D) SM-DTEM imaging, which consists of applying the principles of compressed ultrafast tomographic imaging to recover a time sequence of images from a set of streak images [9,10]. This can be conceptualized by considering a streak image as an overlapping set of images, where the degree of overlap is determined by the sweep rate of the electron beam on the camera. Then by capturing multiple streak images with different sweep rates and sweep directions, redundant information is obtained about the scene, which is analogous to viewing an object at different orientations in tomographic imaging. We will then explain how compressed sensing tomographic image reconstruction algorithms can be applied to this type of data to recover the sequence of images making up the scene. To demonstrate this and study the recovered image quality, we conducted a series of measurements of the electron pulse passing through a Ted Pella gold cross-grating sample. A set of streak images collected in different directions and sweep speeds was acquired for a 1.85-μm-diameter 2D FOV of the sample. This was then input into a two-step iterative shrinkage/thresholding (TwIST) algorithm-based tomographic reconstruction (TTR) algorithm [9,10] to recover a set of 2D images (Fig. 2). The TTR algorithm was able to recover the scene of the electron beam passing through the sample with an imaging speed of ∼2.7 billion fps (i.e., a 370-ps inter-frame interval) and a sequence depth of 140 frames. It should be noted that this inter-frame interval provides more than an order of magnitude improvement over the 7-ns time resolution of 0D SM-DTEM measurements shown in Fig. 1. The high quality of the reconstructed images is seen in the selection of frames shown in Fig. 2. Notably, the contrast of the square grid grating is clearly resolved, as are latex spheres decorating the surface, which are seen as dark circles near the right edge of the FOV. We will then present further quantitative analysis of the recovered image quality and spatio-temporal characterization of the transmitted electron pulse. In summary, the SM-DTEM developed at INRS has been used to study the photoemission process of high charge photoemitted electron pulses. Furthermore, we demonstrate the use of compressed ultrafast tomographic imaging to recover a sequence of 2D images with picosecond time resolution from a set of streak images. This is new functionality complements other high-speed electron microscopy approaches and can enable new views into the dynamics of nanomaterials. 0D SM-DTEM traces of 50-ns photoemitted electron pulses measured for different UV cathode laser pulse energy. Selected frames of 2D SM-DTEM reconstructed scene of 50-ns electron beam passing through gold cross grating sample. Scale bar = 500 nm.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,015
Score d'incertitude au seuil0,050

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,003
Science ouverte0,0020,001
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0150,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,004
Tête enseignante GPT0,280
Écart entre enseignants0,276 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentnon

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