Pith. sign in

REVIEW 2 major objections 6 minor 48 references

Edge-Detected 4DSTEM -- effective low-dose diffraction data acquisition method for nanopowder samples in a SEM instrument

T0 review · 2 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Low-voltage electron diffraction of nanopowders works by scanning only the thin edges of particles.

desk verdict Useful practical advance: edge-masked 4DSTEM in SEM with per-frame peak finding gives real dose and time savings, but the efficiency numbers need correction and the edge-representativeness assumption is acknowledged rather than tested. read the letter →

arxiv 2411.13265 v1 pith:NAQJ7RKO submitted 2024-11-20 physics.app-ph

Nikita Denisov , Andrey Orekhov , Johan Verbeeck This is my paper · ORCID
classification physics.app-ph
keywords 4D-STEMelectrondiffractionscanningmicroscopenanopowderedgedetectionlow-dosemicroscopyvirtualpatternpeakfinding
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that low-voltage electron diffraction of nanopowders in a commercial SEM can be made practical by collecting data only from the thin edges of particles. The authors call this Edge-Detected 4DSTEM (ED4DSTEM). They argue that thick or empty regions waste beam time and dose without adding information, while the perimeter of a particle or agglomerate is typically thin enough to transmit. If true, crystallographic characterization of nanoparticles becomes far faster, less damaging, and accessible in an inexpensive SEM. The paper backs this with a comparison on lithium iron phosphate showing large reductions in points visited, time, storage, and dose, and with a silicon powder test whose virtual ring pattern matches the calculated profile.

What carries the argument

ED4DSTEM's central object is a sparse scan mask that visits only the dilated edges of particles. The mask is produced from a fast, low-dose overview image by denoising, binarising with adaptive thresholding, applying an edge detector, and dilating the result to tolerate drift and positioning error. On the data side, the load-bearing element is a per-frame peak finder, a modified U-net originally built for X-ray diffraction, that records only peak positions and intensities from each 256x256 diffraction frame; these lists are then rendered as a virtual ring diffraction pattern. The combination of edge-only scanning and per-frame peak finding is what delivers the dose, time, and storage reductions while suppressing amorphous background.

What would settle it

Record full-area 4DSTEM and ED4DSTEM on the same core-shell nanopowder, for example oxide-coated metal nanoparticles, and compare the resulting radial profiles: ED4DSTEM would miss the core crystal reflections, exposing the edge-representative assumption.

Watch

Extended reading notes

Core claim

The paper claims that for nanopowder samples at low acceleration voltage, the only regions that produce useful transmission diffraction are the thin perimeter regions, and that a diffraction experiment can therefore skip the interior of particles and agglomerates entirely. On a lithium iron phosphate agglomerate, ED4DSTEM with a one-pixel perimeter visits roughly 64 times fewer probe positions than full 4DSTEM, with about 45 times less acquisition time, 32 times less applied electron dose, and a correspondingly smaller dataset, while the diffraction frames recorded at matching perimeter positions are identical in quality. The authors further claim that per-frame peak finding, rather than averaging raw patterns first, removes the amorphous support and inelastic background and produces a clean virtual powder ring pattern. On crushed silicon powder, the experimental 30 keV ED4DSTEM ring profile matches the theoretical silicon diffraction profile in both peak positions and intensity ratios.

Load-bearing premise

The method assumes the thin edges of a particle or agglomerate are representative of the whole sample, so data from edges stands in for the bulk.

Editorial extensions

If this is right

  • Nanopowder phase identification and orientation mapping become practical in a conventional SEM at 30 keV, yielding powder-like ring patterns from tiny sample volumes.
  • Beam-sensitive materials in a TEM can use the same edge-only scan strategy to cut dose and contamination while preserving crystallographic information.
  • Storing only peak positions and intensities shrinks diffraction datasets by large factors and allows on-the-fly processing during acquisition.
  • The efficiency gain grows with particle size: larger particles have a smaller perimeter-to-area ratio, so the relative dose reduction is larger.
  • A hybrid analysis that combines the summed raw pattern with the virtual peak pattern can separate crystalline and amorphous content, which is difficult in powder XRD.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • This suggests the same mask idea could apply to any beam-sensitive specimen with heterogeneous thickness, not just powders, as long as a thin edge carries the structural information.
  • Combining ED4DSTEM with event-based detectors could push dose and speed further, but event-rate limits may force lower beam currents, trading speed for dose.
  • A systematic study comparing ED4DSTEM and full-area patterns on samples with known surface layers would quantify when the edge-representative assumption breaks.
  • Per-frame peak finding may also improve high-voltage TEM diffraction of mixed amorphous and crystalline specimens, since it discards the amorphous halo before averaging.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper proposes ED4DSTEM, a data-acquisition strategy for 4DSTEM in a commercial SEM at low acceleration voltage. The method first records a fast overview image, applies denoising and Canny edge detection with dilation, and then scans only the dilated perimeter mask while recording diffraction patterns. The authors claim this reduces acquisition time, electron dose, contamination, and storage relative to a full-area scan while preserving the crystallographic information available at the sample edge. The workflow is demonstrated on a LiFePO4 agglomerate for the efficiency comparison and on crushed Si powder for the diffraction-data quality, using a peak-finding pipeline based on a modified PeakNet network to build virtual ring patterns. The Si virtual pattern matches the theoretical Si powder profile well. The paper explicitly acknowledges that the method relies on the assumption that edges are representative of the whole sample, noting that this fails for core-shell or coated particles.

Significance. If the method performs as claimed, it offers a practical route to low-dose, low-storage electron diffraction of nanopowders in relatively inexpensive SEM instruments, which is relevant for beam-sensitive and statistically demanding applications. The Si powder demonstration is a convincing proof-of-concept, and the peak-finding approach to suppress amorphous background is a useful contribution. The authors are transparent about the representativeness limitation, but the generality implied by the title and abstract is broader than what the current experiments establish. The quantitative efficiency claims also contain a reporting inconsistency in Section 3.2 that needs correction. Overall, the core idea is sound and the experimental demonstration is credible, but the scope of the central claim requires either additional validation or explicit restriction.

major comments (2)
  1. [Section 3.2, Table 1] The text reporting the performance ratios is inconsistent with Table 1. The text states that the total experiment time and storage space are reduced by approximately 45 and 12 times, respectively, for the 1-pixel and 3-pixel perimeter cases. From Table 1, the time ratios are 232/3.7 ≈ 63 and 232/14 ≈ 17, while the storage ratios are 3756/82.8 ≈ 45 and 3756/314 ≈ 12. The “45 and 12” values correspond to storage only, not to time. Please correct the text or clarify the correspondence. In addition, the efficiency comparison is based on a single LiFePO4 agglomerate without error estimates or replicate measurements; the authors call the comparison “qualitative,” but the quantitative factors are presented as concrete values. A brief statement of expected variability or an additional example would strengthen the claim.
  2. [Section 4] The central assumption that edges are representative of the whole particle or agglomerate is identified but not tested. The authors state that “we assume edges of our sample are representative for the whole” and acknowledge that edge structure can differ from the bulk and that coatings can prevent accurate analysis. However, no experimental evidence is provided to validate this assumption for any sample with known surface/bulk differences, and the title/abstract present the method as effective for nanopowder samples generally. Since the usefulness of ED4DSTEM for a particular sample depends critically on this assumption, the manuscript should either (a) include a comparison of edge versus interior diffraction for a sample where the assumption is expected to hold and for one where it fails (e.g., a coated particle), or (b) explicitly restrict the method’s applicability to homogeneous, uncoated particles and adjust the title/claims accordingly. Without this, the claim that ED4DSTEM provides “identical diffraction data” is only established for the perimeter locations themselves, not that the perimeter represents the bulk.
minor comments (6)
  1. [Section 2.1] The sentence “Minimal and maximal camera length reachable with our setup was from 10 to 90 mm with reciprocal space coverage from 1.1 to 2.26 Å−1 respectively” is ambiguous: please clarify which camera length corresponds to which reciprocal-space coverage value.
  2. [Section 2.2] There are several typographical errors, including “meaningfull” (meaningful) and “aquisition” (acquisition) in the description of the ED4DSTEM procedure.
  3. [Section 2.3] The text contains typos such as “parrallel” (parallel) and “amorhpous/cristalline” (amorphous/crystalline); a proofreading pass is needed.
  4. [Section 3.2] The phrase “≈ 64 and ≈ 17 times less points” should be “≈64 and ≈17 times fewer points,” and “1- and 3-pixels perimeter” should be “1- and 3-pixel perimeter.”
  5. [Section 3.1] The statement that “for a wide range of different powders samples typical values for the transmission maximum were in the order of 90-140 nm for a 30 kV electron beam” is not supported by any data shown in the paper; either provide the supporting measurements or label this as an unpublished observation.
  6. [Section 2.3] The modification of PeakNet is described only as “changed the neural network output processing”; for reproducibility, please provide more detail on the architectural or output-layer change and indicate whether the modified code is available.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central ED4DSTEM validation is against an external simulated Si powder profile, and the paper's self-citations are not load-bearing.

full rationale

The derivation chain is not circular. The method's central proposal is to collect diffraction only from edge regions defined by a denoised, edge-detected mask (Sec. 2.2). This scan mask is chosen from a fast overview image, and the diffraction data at those positions are then compared with a full 4DSTEM scan (Sec. 3.2) and with a simulated Si powder profile (Sec. 3.3). The Si comparison is an independent external benchmark: the theoretical profile is computed by CrystalDiffract from a known Si structure, not from the experimental frames or from any parameter fitted in this work. The modified PeakNet peak finder originates from an external publication [38], so the data-reduction step does not rest on a self-citation. The paper's self-references ([24], [25], [29]) support hardware and image-denoising components; none of them defines the crystallographic outcome. One statement in Sec. 3.2, that ED4DSTEM 'provides identical diffraction data ... at the perimeter locations as normal 4DSTEM,' is essentially by construction because ED4DSTEM is a subset of the 4DSTEM raster with the same acquisition settings; however, the paper does not use this subset identity as the main validation, and the Si profile match is the independent check. The explicitly acknowledged assumption that edge regions represent the whole sample (Sec. 4) is a limitation in scope, e.g., for core-shell particles, rather than a circular step: it is stated as an assumption rather than derived from the data. The table/text efficiency ratio discrepancy is a reporting inconsistency, not circularity.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces a named workflow (ED4DSTEM) and a virtual ring diffraction pattern, but no new physical entities, particles, forces, or conserved quantities. The free parameters are image-processing choices rather than fitted physics.

free parameters (2)
  • dilation_kernel_size
    The dilation kernel applied to the edge mask is a hand-chosen trade-off between robustness to drift and including areas that are too thick or too thin to give useful diffraction (Section 2.2, Fig. 5).
  • perimeter_thickness_pixels = 1 and 3
    The authors compare a 1-pixel and a 3-pixel dilated perimeter as the ED4DSTEM scan region; the choice affects the efficiency and quality trade-off (Section 3.2, Table 1).
assumptions (4)
  • domain assumption The inelastic mean free path model (Eq. 1) from Malis et al. applies at 30 keV and motivates the thickness limits.
    Used in Section 2.2 to motivate why low-voltage diffraction needs thin samples; not derived in this paper.
  • domain assumption The thin edges of particles are representative of the bulk material.
    Explicitly stated in Section 4 as a requirement; if false (core-shell particles, surface-reconstructed nanoparticles), the method produces biased data. This is the weakest assumption.
  • domain assumption Edge detection on the denoised overview image identifies regions of adequate electron transparency.
    The whole ED4DSTEM workflow assumes that the perimeter visible in the overview image corresponds to areas where the sample is thin enough for transmission diffraction (Section 2.2).
  • domain assumption Per-frame peak finding removes amorphous background without systematically losing weak crystalline peaks.
    The virtual ring pattern is built only from detected peaks; if the neural network misses weak peaks, the intensity ratios would be biased. The paper compares to theoretical Si and claims good agreement (Section 3.3).

how reviews work

0 comments
Cite this review

Pith. "Pith review of Edge-Detected 4DSTEM -- effective low-dose diffraction data acquisition method for nanopowder samples in a SEM instrument." pith.science (2026). https://pith.science/paper/NAQJ7RKO

@misc{pith2026241113265,
  author       = {Pith},
  title        = {Pith review of: Edge-Detected 4DSTEM -- effective low-dose diffraction data acquisition method for nanopowder samples in a SEM instrument},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NAQJ7RKO}},
  note         = {Machine review of arXiv:2411.13265}
}
read the original abstract

The appearance of direct electron detectors marked a new era for electron diffraction. Their high sensitivity and low noise opens the possibility to extend electron diffraction from transmission electron microscopes (TEM) to lower energies such as those found in commercial scanning electron microscopes (SEM).The lower acceleration voltage does however put constraints on the maximum sample thickness and it is a-priori unclear how useful such a diffraction setup could be. On the other hand, nanoparticles are increasingly appearing in consumer products and could form an attractive class of naturally thin samples to investigate with this setup.In this work we present such a diffraction setup and discuss methods to effectively collect and process diffraction data from dispersed crystalline nanoparticles in a commercial SEM instrument. We discuss ways to drastically reduce acquisition time while at the same time lowering beam damage and contamination issues as well as providing significant data reduction leading to fast processing and modest data storage needs. These approaches are also amenable to TEM and could be especially useful in the case of beam-sensitive objects.

Figures

Figures reproduced from arXiv: 2411.13265 by the authors.

Figure 1
Figure 1. Experimental set up for transmission (diffraction) studies in SEM [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Estimation of transmission thickness limit at 30 keV accelerating voltage on [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Sketch of 2 situations where the particle or particles agglomerate is too thick [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Effect of denoising on edge detection quality: a) overview image taken at [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Illustration of the process steps to obtain the ED4DSTEM scan positions [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Lithium Iron Phosphate (LiF eP O4) 4DSTEM taken at HT = 30keV and BC = 50pA with diffraction examples: a) at the edge of agglomerate, b) at the middle part of agglomerate [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 7
Figure 7. Figure 7: a) 4DSTEM and b) ED4DSTEM images of Lithium Iron Phosphate taken [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]
Figure 8
Figure 8. Figure 8: Example of large area (FOV 60 µm2 ) ED4DSTEM experiment on Si powder: a) ETD overview image, b) Detected scan mask for ED4DSTEM experiment [PITH_FULL_IMAGE:figures/full_fig_p010_8.png]
Figure 9
Figure 9. Figure 9: ED4DSTEM Diffraction data processing and data evaluation with theoretical [PITH_FULL_IMAGE:figures/full_fig_p010_9.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

48 extracted references · 45 canonical work pages

  1. [1]

    Henderson

    R. Henderson. The potential and limitations of neutrons, electrons and X-rays for atomic resolution microscopy of unstained biological molecules. Quarterly Reviews of Biophysics, 28(2):171–193, May 1995

    work page 1995

  2. [2]

    Tim Gruene, Julian T. C. Wennmacher, Christan Zaubitzer, Julian J. Holstein, Jonas Heidler, Ariane Fecteau-Lefebvre, Sacha De Carlo, Elisabeth M¨ uller, Kenneth N. Goldie, Irene Regeni, Teng Li, Gustavo Santiso-Quinones, Gunther Steinfeld, Stephan Hand- schin, Eric van Genderen, Jeroen A. van Bokhoven, Guido H. Clever, and Radosav Pan- telic. Rapid Struct...

    work page

  3. [3]

    Vincent and P

    R. Vincent and P. A. Midgley. Double conical beam-rocking system for measurement of integrated electron diffraction intensities. Ultramicroscopy, 53(3):271–282, March 1994

    work page 1994

  4. [4]

    U. Kolb, Y. Krysiak, and S. Plana-Ruiz. Automated electron diffraction tomography – development and applications. Acta Crystallographica Section B: Structural Science, Crystal Engineering and Materials, 75(4):463–474, August 2019. Publisher: International Union of Crystallography

    work page 2019

  5. [5]

    Klar, Ya¸ sar Krysiak, Hongyi Xu, Gwladys Steciuk, Jung Cho, Xiaodong Zou, and Lukas Palatinus

    Paul B. Klar, Ya¸ sar Krysiak, Hongyi Xu, Gwladys Steciuk, Jung Cho, Xiaodong Zou, and Lukas Palatinus. Accurate structure models and absolute configuration determination using dynamical effects in continuous-rotation 3D electron diffraction data. Nature Chemistry, 15(6):848–855, June 2023. Publisher: Nature Publishing Group

    work page 2023

  6. [6]

    Z. G. Pinsker. Electron Diffraction. Butterworths Scientific Publications, 1953. Google- Books-ID: TbsmAAAAMAAJ

    work page 1953

  7. [7]

    Z. G. Pinsker and Lewis B. Leder. Electron Diffraction Structure Analysis and the Investigation of Semiconducting Materials. In L. Marton and Claire Marton, editors, Advances in Electronics and Electron Physics , volume 11, pages 355–412. Academic Press, January 1959

    work page 1959

  8. [8]

    Sieger, U

    P. Sieger, U. Werthmann, and S. Saouane. The polymorph landscape of dabigatran etexilate mesylate: Taking the challenge to bring a metastable polymorph to market. European Journal of Pharmaceutical Sciences , 186:106447, July 2023

    work page 2023

Show all 48 references
  1. [9]

    White, Eiji Okunishi, Yoshitaka Aoyama, Akihito Yamano, Hiroyasu Sato, Joseph D

    Sho Ito, Fraser J. White, Eiji Okunishi, Yoshitaka Aoyama, Akihito Yamano, Hiroyasu Sato, Joseph D. Ferrara, Micha l Jasnowski, and Mathias Meyer. Structure determina- tion of small molecule compounds by an electron diffractometer for 3D ED/MicroED. CrystEngComm, 23(48):8622–8...

    2021

  2. [10]

    Establishing a relativistic ultrafast electron diffraction & imaging (RUEDI) UK national facility

    Julian McKenzie and others. Establishing a relativistic ultrafast electron diffraction & imaging (RUEDI) UK national facility. JACoW, IPAC2023:TUPL144, 2023

    2023

  3. [11]

    R. N. Clough, G. Moldovan, and A. I. Kirkland. Direct Detectors for Electron Mi- croscopy. Journal of Physics: Conference Series , 522(1):012046, June 2014. 14

    2014

  4. [12]

    Barnaby D. A. Levin. Direct detectors and their applications in electron microscopy for materials science. Journal of Physics: Materials , 4(4):042005, July 2021. Publisher: IOP Publishing

    2021

  5. [13]

    McMullan, A

    G. McMullan, A. R. Faruqi, and R. Henderson. Chapter One - Direct Electron Detec- tors. In R. A. Crowther, editor, Methods in Enzymology , volume 579 of The Resolution Revolution: Recent Advances In cryoEM , pages 1–17. Academic Press, January 2016

    2016

  6. [14]

    A. R. Faruqi, D. M. Cattermole, R. Henderson, B. Mikulec, and C. Raeburn. Evaluation of a hybrid pixel detector for electron microscopy. Ultramicroscopy, 94(3):263–276, April 2003

    2003

  7. [15]

    Fast Pixelated Detectors in Scanning Transmission Electron Microscopy

    Magnus Nord, Robert W H Webster, Kirsty A Paton, Stephen McVitie, Damien Mc- Grouther, Ian MacLaren, and Gary W Paterson. Fast Pixelated Detectors in Scanning Transmission Electron Microscopy. Part I: Data Acquisition, Live Processing, and Stor- age. Microscopy and Microanalys...

    2020

  8. [16]

    High Dynamic Range Pixel Array Detector for Scanning Transmission Electron Microscopy

    Mark W Tate, Prafull Purohit, Darol Chamberlain, Kayla X Nguyen, Robert Hovden, Celesta S Chang, Pratiti Deb, Emrah Turgut, John T Heron, Darrell G Schlom, Daniel C Ralph, Gregory D Fuchs, Katherine S Shanks, Hugh T Philipp, David A Muller, and Sol M Gruner. High Dynamic Range...

    2016

  9. [17]

    wet-STEM

    A. Bogner, P. H. Jouneau, G. Thollet, D. Basset, and C. Gauthier. A history of scanning electron microscopy developments: Towards “wet-STEM” imaging. Micron, 38(4):390– 401, June 2007

    2007

  10. [18]

    Caplins, Jason D

    Benjamin W. Caplins, Jason D. Holm, and Robert R. Keller. Transmission imaging with a programmable detector in a scanning electron microscope. Ultramicroscopy, 196:40–48, January 2019

    2019

  11. [19]

    On the Progress of Scanning Transmission Electron Microscopy (STEM) Imaging in a Scanning Electron Microscope

    Cheng Sun, Erich M¨ uller, Matthias Meffert, and Dagmar Gerthsen. On the Progress of Scanning Transmission Electron Microscopy (STEM) Imaging in a Scanning Electron Microscope. Microscopy and Microanalysis, 24(2):99–106, April 2018

    2018

  12. [20]

    Orekhov, D

    A. Orekhov, D. Jannis, N. Gauquelin, G. Guzzinati, A. Nalin Mehta, S. Psilodimi- trakopoulos, L. Mouchliadis, P. K. Sahoo, I. Paradisanos, A. C. Ferrari, G. Kioseoglou, E. Stratakis, and J. Verbeeck. Wide field of view crystal orientation mapping of layered materials, November...

    2020

  13. [21]

    Powder Nano- Beam Diffraction in Scanning Electron Microscope: Fast and Simple Method for Analysis of Nanoparticle Crystal Structure

    Miroslav Slouf, Radim Skoupy, Ewa Pavlova, and Vladislav Krzyzanek. Powder Nano- Beam Diffraction in Scanning Electron Microscope: Fast and Simple Method for Analysis of Nanoparticle Crystal Structure. Nanomaterials, 11(4):962, April 2021. Number: 4 Publisher: Multidisciplinar...

    2021

  14. [22]

    Exploring 4DSTEM-in-SEM: From Imple- mentation to Material Characterization

    Johannes M¨ uller and Christoph Koch. Exploring 4DSTEM-in-SEM: From Imple- mentation to Material Characterization. Microscopy and Microanalysis , 30(Supple- ment 1):ozae044.315, July 2024

    2024

  15. [23]

    Low energy nano diffraction (LEND) – A versatile diffraction technique in SEM

    Peter Schweizer, Peter Denninger, Christian Dolle, and Erdmann Spiecker. Low energy nano diffraction (LEND) – A versatile diffraction technique in SEM. Ultramicroscopy, 213:112956, June 2020

    2020

  16. [24]

    Low-cost electron detector for scanning electron microscope

    Evgenii Vlasov, Nikita Denisov, and Johan Verbeeck. Low-cost electron detector for scanning electron microscope. HardwareX, 14:e00413, June 2023

    2023

  17. [25]

    Characterization of a Timepix detector for use in SEM acceleration voltage range

    Nikita Denisov, Daen Jannis, Andrey Orekhov, Knut M¨ uller-Caspary, and Johan Ver- beeck. Characterization of a Timepix detector for use in SEM acceleration voltage range. Ultramicroscopy, 253:113777, November 2023

    2023

  18. [26]

    Malis, S

    T. Malis, S. C. Cheng, and R. F. Egerton. EELS log-ratio tech- nique for specimen-thickness measurement in the TEM. Jour- nal of Electron Microscopy Technique , 8(2):193–200, 1988. eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/jemt.1060080206

    1988 doi

  19. [27]

    Zhang, R

    H. Zhang, R. Egerton, and M. Malac. EELS Investigation of the Formulas for Inelastic Mean Free Path. Microscopy and Microanalysis, 17(S2):1466–1467, July 2011

    2011

  20. [28]

    Brief review of image denoising techniques

    Linwei Fan, Fan Zhang, Hui Fan, and Caiming Zhang. Brief review of image denoising techniques. Visual Computing for Industry, Biomedicine, and Art , 2, December 2019

    2019

  21. [29]

    Lobato, T

    I. Lobato, T. Friedrich, and S. Van Aert. Deep convolutional neural networks to re- store single-shot electron microscopy images. npj Computational Materials , 10(1):1–19, January 2024. Publisher: Nature Publishing Group. 15

    2024

  22. [30]

    A Threshold Selection Method from Gray-Level Histograms

    Nobuyuki Otsu. A Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics , 9(1):62–66, January 1979. Conference Name: IEEE Transactions on Systems, Man, and Cybernetics

    1979

  23. [31]

    Mardia and T.J

    K.V. Mardia and T.J. Hainsworth. A spatial thresholding method for image segmenta- tion. IEEE Transactions on Pattern Analysis and Machine Intelligence , 10(6):919–927, November 1988. Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence

    1988

  24. [32]

    A Computational Approach To Edge Detection

    John Canny. A Computational Approach To Edge Detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on , PAMI-8:679–698, December 1986

    1986

  25. [33]

    Realtime Com- puter Vision with OpenCV: Mobile computer-vision technology will soon become as ubiquitous as touch interfaces

    Kari Pulli, Anatoly Baksheev, Kirill Kornyakov, and Victor Eruhimov. Realtime Com- puter Vision with OpenCV: Mobile computer-vision technology will soon become as ubiquitous as touch interfaces. Queue, 10(4):40–56, April 2012

    2012

  26. [34]

    Lobato and D

    I. Lobato and D. Van Dyck. An accurate parameterization for scattering factors, electron densities and electrostatic potentials for neutral atoms that obey all phys- ical constraints. Acta Crystallographica Section A , 70(6):636–649, 2014. eprint: https://onlinelibrary.wiley.c...

    2014 doi

  27. [35]

    Liz-Marz´ an, and Sara Bals

    Adri´ an Pedrazo-Tardajos, Nathalie Claes, Da Wang, Ana S´ anchez-Iglesias, Proloy Nandi, Kellie Jenkinson, Robin De Meyer, Luis M. Liz-Marz´ an, and Sara Bals. Direct visualization of ligands on gold nanoparticles in a liquid environment.Nature Chemistry, 16(8):1278–1285, Aug...

    2024

  28. [36]

    Exploring the effects of graphene and temperature in reducing electron beam damage: A TEM and electron diffraction-based quantitative study on Lead Phthalocyanine (PbPc) crystals

    Noopur Jain, Yansong Hao, Urvi Parekh, Martin Kaltenegger, Adri´ an Pedrazo-Tardajos, Roberto Lazzaroni, Roland Resel, Yves Henri Geerts, Sara Bals, and Sandra Van Aert. Exploring the effects of graphene and temperature in reducing electron beam damage: A TEM and electron diff...

    2023

  29. [37]

    MacArthur, Duncan N

    Francisco de la Pe˜ na, Eric Prestat, Vidar Tonaas Fauske, Jonas L¨ ahnemann, Pierre Burdet, Petras Jokubauskas, Tom Furnival, Carter Francis, Magnus Nord, Tomas Osta- sevicius, Katherine E. MacArthur, Duncan N. Johnstone, Mike Sarahan, Joshua Taillon, Thomas Aarholt, pquinn d...

    2024

  30. [38]

    PeakNet: An Autonomous Bragg Peak Finder with Deep Neural Networks, June 2023

    Cong Wang, Po-Nan Li, Jana Thayer, and Chun Hong Yoon. PeakNet: An Autonomous Bragg Peak Finder with Deep Neural Networks, June 2023. arXiv:2303.15301

    2023 arXiv

  31. [39]

    pyxem/pyxem: v0.19.1, June 2024

    Duncan Johnstone, Phillip Crout, Carter Francis, Magnus Nord, Joonatan Laulainen, Simon Høg ˚ as, Eirik Opheim, Eric Prestat, Ben Martineau, Tina Bergh, Niels Cautaerts, H ˚ akon Wiik˚Anes, Stef Smeets, Viljar Johan Femoen, Andrew Ross, Joseph Broussard, Shuoyuan Huang, Sean C...

    2024

  32. [40]

    py4DSTEM: A Software Package for Four-Dimensional Scanning Transmission Electron Microscopy Data Analysis

    Benjamin H Savitzky, Steven E Zeltmann, Lauren A Hughes, Hamish G Brown, Shiteng Zhao, Philipp M Pelz, Thomas C Pekin, Edward S Barnard, Jennifer Donohue, Luis Rangel DaCosta, Ellis Kennedy, Yujun Xie, Matthew T Janish, Matthew M Schnei- der, Patrick Herring, Chirranjeevi Gopa...

    2021

  33. [41]

    Robert B¨ ucker, Pascal Hogan-Lamarre, and R. J. Dwayne Miller. Serial Electron Diffraction Data Processing With diffractem and CrystFEL. Frontiers in Molecular Biosciences, 8, May 2021. Publisher: Frontiers

    2021

  34. [42]

    Jannis, C

    D. Jannis, C. Hofer, C. Gao, X. Xie, A. B´ ech´ e, T. J. Pennycook, and J. Verbeeck. Event driven 4D STEM acquisition with a Timepix3 detector: Microsecond dwell time and faster scans for high precision and low dose applications. Ultramicroscopy, 233:113423, March 2022. 16

    2022

  35. [43]

    S’ari, J

    M. S’ari, J. Cattle, N. Hondow, H. Blade, S. Cosgrove, R. M. Brydson, and A. P. Brown. Analysis of Electron Beam Damage of Crystalline Pharmaceutical Materials by Trans- mission Electron Microscopy. Journal of Physics: Conference Series , 644(1):012038, September 2015. Publish...

    2015

  36. [44]

    CHAPTER 1 - BASIC PROPERTIES AND MEASURING METHODS OF NANOPARTICLES

    Toyokazu Yokoyama, Hiroaki Masuda, Michitaka Suzuki, Kensei Ehara, Kiyoshi Nogi, Masayoshi Fuji, Takehisa Fukui, Hisao Suzuki, Junichi Tatami, Kazuyuki Hayashi, and Kenji Toda. CHAPTER 1 - BASIC PROPERTIES AND MEASURING METHODS OF NANOPARTICLES. In Masuo Hosokawa, Kiyoshi Nogi...

    2008

  37. [45]

    Lupini, Scott D

    Ryo Ishikawa, Andrew R. Lupini, Scott D. Findlay, and Stephen J. Pennycook. Quantita- tive Annular Dark Field Electron Microscopy Using Single Electron Signals. Microscopy and Microanalysis, 20(1):99–110, February 2014

    2014

  38. [46]

    Krivanek, Niklas Dellby, Matthew F

    Ondrej L. Krivanek, Niklas Dellby, Matthew F. Murfitt, Matthew F. Chisholm, Timo- thy J. Pennycook, Kazutomo Suenaga, and Valeria Nicolosi. Gentle STEM: ADF imag- ing and EELS at low primary energies. Ultramicroscopy, 110(8):935–945, July 2010

    2010

  39. [47]

    Poikela, J

    T. Poikela, J. Plosila, T. Westerlund, M. Campbell, M. De Gaspari, X. Llopart, V. Gro- mov, R. Kluit, M. van Beuzekom, F. Zappon, V. Zivkovic, C. Brezina, K. Desch, Y. Fu, and A. Kruth. Timepix3: a 65K channel hybrid pixel readout chip with simultaneous ToA/ToT and sparse read...

    2014

  40. [2018]

    eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/anie.201811318

    doi

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.