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Sanghyo
Lee
,
Sojin
Kim
,
Jinseok
Ryu
,
Jaewook
Lee
,
Jinseok
Hong
,
Ji Eun
Kim
,
Ju-Young
Cha
,
Yunho
Shin
,
Daewoong
Kwon
,
Jung Ho
Yoon
,
Min Hyuk
Park
,
Miyoung
Kim
,
Seung-Yong
Lee
Abstract: Understanding electric-field-induced phase transitions is crucial for optimizing the ferroelectric and antiferroelectric properties of hafnium zirconium oxide (Hf0.5Zr0.5O2, HZO) thin films. Here, we use in situ transmission electron microscopy (TEM) to uncover the nanoscale mechanism of field-induced phase evolution in ultrathin HZO films at the morphotropic phase boundary (MPB), directly visualizing oxygen vacancy migration and its correlation with the transformation from the nonpolar tetragonal to polar orthorhombic phase. Our in situ TEM setup applied sub-100 μs bipolar voltage pulses, mimicking real device operation while allowing the detection of the subtle changes induced by such short pulses. Unsupervised machine learning analysis of electron energy-loss spectroscopy spectrum images (EELS-SIs) revealed distinct spectral features associated with local structural evolution, with quantitative results confirming oxygen-deficient regions aligned with orthorhombic phase formation. Unlike conventional TEM studies confined to a few nanoscale domains, this approach enables film-scale interpretation of phase evolution, capturing broader trends beyond isolated observations. Concurrent oxygen content changes in the TiN electrode further indicate active vacancy exchange between HZO and TiN under bias. These findings directly link oxygen vacancy dynamics to polarization switching, offering critical guidance for stabilizing ferroelectric phases and advancing next-generation memory and logic devices.
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Feb 2026
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I24-Microfocus Macromolecular Crystallography
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Diamond Proposal Number(s):
[25587]
Open Access
Abstract: Nonstructural protein 3 (nsp3) is crucial for SARS-CoV-2 infection. It is the largest protein of the virus with roughly 2000 residues, and a major drug target. However, because of its size, disordered regions, and transmembrane domains, the atomic structure of the whole protein has not yet been established. Only 10 out of its 16 domains were individually determined in experiments. Here, we demonstrate how structural bioinformatics, AI-based fold prediction, and traditional experiments complement each other and can shed light on the makeup of this important protein, both in SARS-CoV-2 and in related viruses. Our method can be generalized for other multidomain proteins. Our prediction-based approach reveals a previously undescribed folded domain, which we could confirm experimentally. Our research also suggests a potential function of the domain Y1: this domain may be involved in the assembly of nsp3, nsp4, and nsp6 into the hexameric pore, which was discovered by electron tomography and exports RNA into the cytosol. The Y1 hexamer, however, could not be expressed on its own. We revise domain segmentation and nomenclature of nsp3 domains.
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Feb 2026
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I22-Small angle scattering & Diffraction
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Sasha
Murokh
,
Alexander
Alekseev
,
Viacheslav
Kubytskyi
,
Viacheslav
Shcherbakov
,
Oleksii
Avdieiev
,
Sergey A.
Denisov
,
Ashkan
Ajeer
,
Lois
Adams
,
Charlene
Greenwood
,
Keith
Rogers
,
Lev
Mourokh
,
Pavel
Lazarev
Diamond Proposal Number(s):
[24977]
Open Access
Abstract: Structural biomarkers determined by X-ray scattering of the tissues can complement conventional histopathology and facilitate a fast triage procedure of cancer biopsy samples. It has been shown previously that lipid reflexes can distinguish cancerous from benign samples, except for fibroadenomas. In the present study, we demonstrate that fibroadenoma samples can be recognized using X-ray scattering of collagen. Moreover, we show that modifications in collagen structure are manifested in the water reflexes. Examination of diffraction patterns from water using two-dimensional Fourier transformation and machine learning yields excellent classification metrics in both synchrotron images and laboratory diffractometer data.
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Feb 2026
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E02-JEM ARM 300CF
I05-ARPES
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Amy
Carl
,
Nicholas
Clark
,
David G.
Hopkinson
,
Matthew
Hamer
,
Matthew
Watson
,
Laxman
Nagireddy
,
James E.
Nunn
,
Alexei
Barinov
,
Yichao
Zou
,
William
Thornley
,
Casey
Cheung
,
Wendong
Wang
,
Sam
Sullivan-Allsop
,
Xiao
Li
,
Astrid
Weston
,
Eli G.
Castanon
,
Andrey V.
Kretinin
,
Cephise
Cacho
,
Neil R.
Wilson
,
Sarah J.
Haigh
,
Roman
Gorbachev
Diamond Proposal Number(s):
[21597, 21981, 24290, 24338]
Open Access
Abstract: Magnetic two-dimensional materials are a promising platform for novel nano-electronic device architectures. One such layered crystal is the ferromagnetic semiconductor chromium germanium telluride (Cr2Ge2Te6) which recently attracted interest due to its potential for spintronics and memory applications. Here we investigate its properties from the structural standpoint using atomic resolution Scanning Transmission Electron Microscopy (STEM) and present the first atomic resolution images down to its monolayer limit. We develop a novel technique that allows one to map the local tilt with unprecedented spatial resolution using only high-resolution images, enabling mapping of the topography and morphological variation of atomically thin crystals. Using it, we show that the Cr2Ge2Te6 monolayer has an unusually large out-of-plane rippling, with local tilt variation reaching 20° over few nm length scales. We hypothesize that such a strongly buckled structure originates from both point and extended lattice defects which are more prevalent in monolayer crystals. In addition, we correlate the structural observations with the band structure measurements using Angle-Resolved Photoemission Spectroscopy (ARPES). We believe that both the atomic scale insights we have gained on Cr2Ge2Te6 and our novel approach to nanoscale topography mapping will benefit the development of van der Waals heterostructures in both fundamental and applied research.
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Feb 2026
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Justice O.
Amofa
,
Juergen
George
,
Noella A.
Okumu
,
Moses
Ohene
,
Ermias M.
Terefe
,
Stany L.
Tsomene
,
Oluwatodimu C.
Tougue
,
Ifeoluwa O.
Bejide
,
Kevin C.
Nabukeera
,
Anita Y.
Nelson
,
Carlos S. D.
Tagne
,
Patrick Y.
Osabutey
,
Aminata
Ndiaye
,
David O.
Nkwe
,
Emmanuel C.
Ohaekenyem
,
Tolulope F.
Jolaiya
,
Harrison
Banda
,
Omorede
Ikponmwosa-Eweka
,
Woutouoba
N. David
,
Blessing B.
Ekpenyong
,
Abdoulaye
Segda
,
Oudou
Diabate
,
Aliyi H.
Jarso
,
Kaddu
Arafat
,
Alyaa
Elrashedy
,
Mulatu M.
Yadeta
,
Zipporah B.
Richard
,
Isaac E.
Omara
,
Tshepang
Ndaba
,
Innocentmary I.
Ejiofor
,
Pierre F. R.
Magwell
,
Mohamed
Sedeek
,
Linda O.
Lazaro
,
Regan M.
Nyoni
,
Rossel A.
Oketch
,
Walter
Odur
,
Yaser M.
Hassan
,
Peris
Ambala
,
Courage
Chandipwisa
,
Osim P.
Bassey
,
Laurah N.
Ondari
,
Udokang G.
Jonah
,
Christelle A.
Amoussou
,
Racheal C.
Kyomukama
,
Cedric
Yamssi
,
Sunday C.
James
,
Abdoul K.
Kone
,
Oumar
Ndiaye
,
Henry
Ssenfuka
,
Agatha K.
Nyang’au
,
Yohana
Amos
,
Hakiimu
Kawalya
,
Bernard
Mware
,
Washingtone J.
Adundo
,
Vanessa B.
Ngannang-Fezeu
,
Alphonse G.
Tandja
,
Ahmed H.
Abdellatif
,
Oladokun F.
Omowumi
,
Nsubuga M.
Luutu
,
Angelo K. B.
Kouman
,
Doaa S.
Soliman
,
Nehemiah K.
Essilfie
,
James J.
Wabwile
,
Safiétou
Sankhe
,
Fatoumata G.
Fofana
,
Walid
Heiba
,
Yao
Nasser
,
Appolinaire
Djikeng
,
Eva
Akurut
,
Andrew
Walakira
,
Aurélien F. A.
Moumbock
,
Julia J.
Griese
,
Calvin
Tiengwe
,
Mama
Ndi
,
Itziar S.
Martin
,
Michel
Fodje
,
Nicolas V.
Rüffin
,
Katharina C.
Cramer
,
Jamaine
Davis
,
Emmanuel
Nji
Open Access
Abstract: Artificial intelligence is rapidly transforming structural biology and accelerating access to protein structures, yet many Africa-based scientists still lack infrastructure, training opportunities, and sustained mentorship to fully benefit. Here, we describe BioStruct-Africa’s community-driven framework integrating AlphaFold, experimental structural biology, and computational drug design to train 1000 scientists over the next decade.
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Feb 2026
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I19-Small Molecule Single Crystal Diffraction
|
S.
Chicco
,
E.
Garlatti
,
A.
Mavromagoulos
,
A. B.
Canaj
,
P.
Bonfà
,
A.
Piovano
,
S.
Dey
,
H.
Little
,
A.
Chiesa
,
A. S.
Ivanov
,
I. J.
Onuorah
,
S.
Parsons
,
G.
Rajaraman
,
Tatiana
Guidi
,
M.
Murrie
,
S.
Carretta
Diamond Proposal Number(s):
[16139]
Open Access
Abstract: Molecular nanomagnets have garnered significant attention in recent years thanks to their unique potential in quantum information processing as molecular qudits and in high-density memory encoding as single-molecule magnets. However, fully unlocking the potential of these systems requires a comprehensive understanding of the interplay between the various mechanisms that govern their relaxation dynamics, which remains a noncompletely understood phenomenon. In this work, we employ a cost-effective semi-ab initio approach to model the magnetization relaxation dynamics in a testbed Dy-based single-molecule magnet and determine the effects of applied external pressure on the interplay between various mechanisms, such as coupling with molecular vibrations and quantum tunneling. Ab initio phonon calculations are validated by direct comparison with inelastic neutron scattering experiments, which are used for the first time to investigate pressure-induced modifications of phonons and vibrations in a molecular nanomagnet. The combination of our theoretical approach with different experimental techniques allows us to predict an overall acceleration of the relaxation dynamics under pressure, disentangling the role of different ingredients, such as crystal field axiality and phonons.
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Jan 2026
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Abstract: G Protein-Coupled Receptors (GPCRs) mediate signal transduction across cellular membranes and are major drug targets. Activation of these receptors upon binding of an extracellular ligand involves propagation of structural change across the transmembrane domain to the cytoplasmic G protein partner, a process generally thought to involve dynamic hydrogen(H)-bond networks. Here we present DNET, a graph-based tool and workflow that enables efficient computations of dynamic protein–water H-bond networks. DNET is a fully portable Python tool that reads simulation trajectories, computes graphs of the dynamic protein–water H-bond networks, and generates, for each H-bonding residue, a residue summary that includes water interactions, H-bond time series, histograms, potential of mean force estimates, and the number of conformations of the H-bond. To facilitate estimates of pKa fluctuations within the H-bond network, DNET calls PROPKA and computes, for each titratable residue that is part of the H-bond network, time series and analyses of the pKa estimates. To illustrate the usefulness of DNET we apply it to study the wild-type and two mutations of jumping spider rhodopsin 1, JSR-1, a visual rhodopsin GPCR activated by the photoisomerization of the covalently bound retinal chromophore. The UV–vis data we present here demonstrate that the mutated JSR-1 proteins express, but both have an altered electrostatic environment of the retinal Schiff base. The DNET analyses indicate a highly complex dynamics of the retinal H-bond network, with some H-bonds that have only one conformational mode, and other H-bonds with multiple conformational modes separated by small energy barriers, and pKa fluctuations that associate with the H-bond dynamics. The mutations associate with an altered H-bond network of the retinal Schiff base.
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Jan 2026
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E02-JEM ARM 300CF
|
Diamond Proposal Number(s):
[33252]
Open Access
Abstract: The Noise2Void technique is demonstrated for successful denoising of atomic resolution scanning transmission electron microscopy (STEM) images. The technique is applied to denoising atomic resolution images and videos of gold adatoms on a graphene surface within a graphene liquid-cell, with the denoised experimental data qualitatively demonstrating improved visibility of both the Au adatoms and the graphene lattice. The denoising performance is quantified by comparison to similar simulated data and the approach is found to significantly outperform both total variation and simple Gaussian blurring. Compared to other denoising methods, the Noise2Void technique has the combined advantages that it requires no manual intervention during training or denoising, no prior knowledge of the sample and is compatible with real-time data acquisition rates of at least 45 frames per second.
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Jan 2026
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Abstract: The pursuit of global optimality has confined feature extraction in hyperspectral imaging to convex formulations, despite the known effectiveness of nonconvex models. However, the adoption of nonconvex models remains limited due to the lack of optimization guarantees. This letter bridges this gap by introducing a novel invex-regularized framework for subspace-based feature extraction. Invexity, a generalization of convexity, enables us to incorporate nonconvex penalties that promote spatial piecewise smoothness and noise resilience while retaining global optimality guarantees. Our formulation is an ℓ2 -norm minimization problem regularized by a penalty that combines the ℓ2 -norm with an invex function. Extensive experiments on urban hyperspectral datasets for image classification demonstrate that the proposed invex-based method achieves outstanding classification accuracy, outperforming state-of-the-art methods.
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Jan 2026
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I13-2-Diamond Manchester Imaging
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Kunning
Tang
,
Ryan T.
Armstrong
,
Peyman
Mostaghimi
,
Yufu
Niu
,
Quentin
Meyer
,
Chuan
Zhao
,
Donal P.
Finegan
,
Melissa
Popeil
,
Kamaljit
Singh
,
Hannah
Menke
,
Alexandros
Patsoukis Dimou
,
Tom
Bultreys
,
Arjen
Mascini
,
Mark
Knackstedt
,
Ying
Da Wang
Open Access
Abstract: The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (1011 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.
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Dec 2025
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