Characteristic Plasmon Energies for 2D In2Se3 Phase Identification at Nanoscale.

Two-dimensional (2D) materials with competing polymorphs offer remarkable potential to switch the associated 2D functionalities for novel device applications. Probing their phase transition and competition mechanisms requires nanoscale characterization techniques that can sensitively detect the nucleation of secondary phases down to single-layer thickness. Here we demonstrate nanoscale phase identification on 2D In2Se3 polymorphs, utilizing their distinct plasmon energies that can be distinguished by electron energy-loss spectroscopy (EELS). The characteristic plasmon energies of In2Se3 polymorphs have been validated by first-principles calculations, and also been successfully applied to reveal phase transitions using in situ EELS. Correlating with in situ X-ray diffraction, we further derive a subtle difference in the valence electron density of In2Se3 polymorphs, consistent with their disparate electronic properties. The nanometer resolution and independence of orientation make plasmon-energy mapping a versatile technique for nanoscale phase identification on 2D materials.

Operational Deflection Shape Measurements on Bladed Disks with Continuous Scanning Laser Doppler Vibrometry.

The continuous scanning laser Doppler vibrometry (CSLDV) technique is usually used to evaluate the vibration operational deflection shapes (ODSs) of structures with continuous surfaces. In this paper, an extended CSLDV is demonstrated to measure the non-continuous surface of the bladed disk and to obtain the ODS efficiently. For a bladed disk, the blades are uniformly distributed on a given disk. Although the ODS of each blade can be derived from its response data along the scanning path with CSLDV, the relative vibration direction between different blades cannot be determined from those data. Therefore, it is difficult to reconstruct the complete vibration mode of the whole blade disk. In order to measure the complete ODS of the bladed disk, a method based on ODS frequency response functions (ODS FRFs) has been proposed. While the ODS of each blade is measured by designing the suitable scanning paths in CSLDV, an additional response signal is obtained at a fixed point as the reference signal to identify the relative vibration phase between the blade and the blade of the bladed disk. Finally, a measurement is performed with a simple bladed disk and the results demonstrate the feasibility and effectiveness of the proposed extended CSLDV method.

A semantic information-driven stepwise landslide displacement prediction model.

Landslide prediction is critical for the early warning of a landslide occurrence. Existing stepwise landslide displacement prediction methods are mostly data-driven approaches. However, these models are vulnerable to overfitting, and the low-dimensional numerical features with high numerical volatility prevent them from precisely quantifying the rapid increase in daily displacement in the acceleration phase. Therefore, we propose a semantic information-driven stepwise landslide displacement prediction model comprising an identifier in the displacement phase and a predictor in the acceleration phase. First, the raw landslide monitoring data are converted into text-based semantic information and the semantic features are fused. Subsequently, based on the daily displacement and velocity, we propose a sliding window phase division algorithm to divide the stepwise landslide phase into stationary and acceleration phases. Finally, the landslide displacement phase is identified, and the displacement during the acceleration phase is predicted. The experimental results of the model on the Xinpu and Qingshi landslides in Chongqing, China, show that the proposed model exploits the derived semantic information to identify the landslide acceleration phase qualitatively, and predict the daily displacement of the acceleration phase quantitatively. The proposed model provides a valuable reference for the early warning of stepwise landslides.

Analysis of misidentifications in TEM characterisation of organic-inorganic hybrid perovskite material.

Organic-inorganic hybrid perovskites (OIHPs) have recently emerged as groundbreaking semiconductor materials owing to their remarkable properties. Transmission electron microscopy (TEM), as a very powerful characterisation tool, has been widely used in perovskite materials for structural analysis and phase identification. However, the perovskites are highly sensitive to electron beams and easily decompose into PbX2 (X = I, Br, Cl) and metallic Pb. The electron dose of general high-resolution TEM is much higher than the critical dose of MAPbI3 , which results in universal misidentifications that PbI2 and Pb are incorrectly labelled as perovskite. The widely existed mistakes have negatively affected the development of perovskite research fields. Here misidentifications of the best-known MAPbI3 perovskite are summarised and corrected, then the causes of mistakes are classified and ascertained. Above all, a solid method for phase identification and practical strategies to reduce the radiation damage for perovskite materials have also been proposed. This review aims to provide the causes of mistakes and avoid misinterpretations in perovskite research fields in the future.

Automated operative phase identification in peroral endoscopic myotomy.

BACKGROUND: Artificial intelligence (AI) and computer vision (CV) have revolutionized image analysis. In surgery, CV applications have focused on surgical phase identification in laparoscopic videos. We proposed to apply CV techniques to identify phases in an endoscopic procedure, peroral endoscopic myotomy (POEM). METHODS: POEM videos were collected from Massachusetts General and Showa University Koto Toyosu Hospitals. Videos were labeled by surgeons with the following ground truth phases: (1) Submucosal injection, (2) Mucosotomy, (3) Submucosal tunnel, (4) Myotomy, and (5) Mucosotomy closure. The deep-learning CV model-Convolutional Neural Network (CNN) plus Long Short-Term Memory (LSTM)-was trained on 30 videos to create POEMNet. We then used POEMNet to identify operative phases in the remaining 20 videos. The model's performance was compared to surgeon annotated ground truth. RESULTS: POEMNet's overall phase identification accuracy was 87.6% (95% CI 87.4-87.9%). When evaluated on a per-phase basis, the model performed well, with mean unweighted and prevalence-weighted F1 scores of 0.766 and 0.875, respectively. The model performed best with longer phases, with 70.6% accuracy for phases that had a duration under 5 min and 88.3% accuracy for longer phases. DISCUSSION: A deep-learning-based approach to CV, previously successful in laparoscopic video phase identification, translates well to endoscopic procedures. With continued refinements, AI could contribute to intra-operative decision-support systems and post-operative risk prediction.

Deciphering the Complex Mineralogy of River Sand Deposits through Clustering and Quantification of Hyperspectral X-Ray Maps.

Alluvial mineral sands rank among the most complex subjects for mineral characterization due to the diverse range of minerals present in the sediments, which may collectively contain a daunting number of elements (>20) in major or minor concentrations (>1 wt%). To comprehensively characterize the phase abundance and chemistry of these complex mineral specimens, a method was developed using hyperspectral x-ray and cathodoluminescence mapping in an electron probe microanalyser (EPMA), coupled with automated cluster analysis and quantitative analysis of clustered x-ray spectra. This method proved successful in identifying and quantifying over 40 phases from mineral sand specimens, including unexpected phases with low modal abundance (<0.1%). The standard-based quantification method measured compositions in agreement with expected stoichiometry, with elemental detection limits in the range of <10­1,000 ppm, depending on phase abundance, and proved reliable even for challenging mineral species, such as the multi-rare earth element (REE) bearing mineral xenotime [(Y,REE)PO4] for which 24 elements were analyzed, including 12 overlapped REEs. The mineral identification procedure was also capable of characterizing mineral groups that exhibit significant compositional variability due to the substitution of multiple elements, such as garnets (Mg, Ca, Fe, Mn, Cr), pyroxenes (Mg, Ca, Fe), and amphiboles (Na, Mg, Ca, Fe, Al).

A self-excited threshold autoregressive state-space model for menstrual cycles: Forecasting menstruation and identifying within-cycle stages based on basal body temperature.

The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the basal body temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of hypothermic and hyperthermic phases, possibly as well as the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. The application of the proposed model to a large data set containing 25 622 cycles provided by 3533 women further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting its wide applicability.

Phase identification of individual crystalline particles by combining EDX and EBSD: application to workplace aerosols.

This paper discusses the combined use of electron backscatter diffraction (EBSD) and energy dispersive X-ray microanalysis (EDX) to identify unknown phases in particulate matter from different workplace aerosols. Particles of α-silicon carbide (α-SiC), manganese oxide (MnO) and α-quartz (α-SiO2) were used to test the method. Phase identification of spherical manganese oxide particles from ferromanganese production, with diameter less than 200 nm, was unambiguous, and phases of both MnO and Mn3O4 were identified in the same agglomerate. The same phases were identified by selected area electron diffraction (SAED) in transmission electron microscopy (TEM). The method was also used to identify the phases of different SiC fibres, and both ß-SiC and α-SiC fibres were found. Our results clearly demonstrate that EBSD combined with EDX can be successfully applied to the characterisation of workplace aerosols. Graphical abstract Secondary electron image of an agglomerate of manganese oxide particles collected at a ferromanganese smelter (a). EDX spectrum of the particle highlighted by an arrow (b). Indexed patterns after dynamic background subtraction from three particles shown with numbers in a

Segmentation of elemental EDS maps by means of multiple clustering combined with phase identification.

An imaging concept is proposed for the phase identification and segmentation of elemental map images from energy dispersive spectroscopy. The procedure starts with presegmentation using common clustering algorithms, continues with automated identification of the chemical compositions, followed by their screening by professional expertise. The ultimate phases are finally clustered by applying a minimum Euclidean distance classifier. The potential, performance and limitations of the approach are presented on energy dispersive spectroscopy maps acquired by a scanning electron microscope and conducted on samples produced from cement clinker, natural rock and hydrated cement mortar. Nevertheless, the technique is suitable for arbitrary types of materials and general devices for energy dispersive spectroscopy acquisition. It is an approach for extending common energy dispersive spectroscopy analysis by means of visual examination and ratio plots towards quantitative rating.

Importance of powder diffraction raw data archival in a curated database for materials science applications.

In recent years, there is a significant interest from the crystallographic and materials science communities to have access to raw diffraction data. The effort in archiving raw data for access by the user community is spearheaded by the International Union of Crystallography (IUCr) Committee on Data. In materials science, where powder diffraction is extensively used, the challenge in archiving raw data is different to that from single crystal data, owing to the very nature of the contributions involved. Powder diffraction (X-ray or neutron) data consist of contributions from the material under study as well as instrument specific parameters. Having raw powder diffraction data can be essential in cases of analysing materials with poor crystallinity, disorder, micro structure (size/strain) etc. Here, the initiative and progress made by the International Centre for Diffraction Data (ICDDR) in archiving powder X-ray diffraction raw data in the Powder Diffraction FileTM (PDFR) database is outlined. The upcoming 2025 release of the PDF-5+ database will have more than 20 800 raw powder diffraction patterns that are available for reference.

Phase division and recognition of crystal HRTEM images based on machine learning and deep learning.

The High Resolution Transmission Electron Microscope (HRTEM) images provide valuable insights into the atomic microstructure, dislocation patterns, defects, and phase characteristics of materials. However, the current analysis and research of HRTEM images of crystal materials heavily rely on manual expertise, which is labor-intensive and susceptible to subjective errors. This study proposes a combined machine learning and deep learning approach to automatically partition the same phase regions in crystal HRTEM images. The entire image is traversed by a sliding window to compute the amplitude spectrum of the Fast Fourier Transform (FFT) in each window. The generated data is transformed into a 4-dimensional (4D) format. Principal component analysis (PCA) on this 4D data estimates the number of feature regions. Non-negative matrix factorization (NMF) then decomposes the data into a coefficient matrix representing feature region distribution, and a feature matrix corresponding to the FFT magnitude spectra. Phase recognition based on deep learning enables identifying the phase of each feature region, thereby achieving automatic segmentation and recognition of phase regions in HRTEM images of crystals. Experiments on zirconium and oxide nanoparticle HRTEM images demonstrate the proposed method achieve the consistency of manual analysis. Code and supplementary material are available at https://github.com/rememberBr/HRTEM2.

AI-enhanced X-ray diffraction analysis: towards real-time mineral phase identification and quantification.

The use of convolutional neural networks can revolutionize XRD analysis by significantly reducing processing times. Demonstration against synthetic and real mineral mixture data provide a first assessment of the accuracy of such methods.

Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns.

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X-ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase-identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al-Si-Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well-trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al-Si-Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample.

Crystallographic phase identifier of a convolutional self-attention neural network (CPICANN) on powder diffraction patterns.

Spectroscopic data, particularly diffraction data, are essential for materials characterization due to their comprehensive crystallographic information. The current crystallographic phase identification, however, is very time consuming. To address this challenge, we have developed a real-time crystallographic phase identifier based on a convolutional self-attention neural network (CPICANN). Trained on 692 190 simulated powder X-ray diffraction (XRD) patterns from 23 073 distinct inorganic crystallographic information files, CPICANN demonstrates superior phase-identification power. Single-phase identification on simulated XRD patterns yields 98.5 and 87.5% accuracies with and without elemental information, respectively, outperforming JADE software (68.2 and 38.7%, respectively). Bi-phase identification on simulated XRD patterns achieves 84.2 and 51.5% accuracies, respectively. In experimental settings, CPICANN achieves an 80% identification accuracy, surpassing JADE software (61%). Integration of CPICANN into XRD refinement software will significantly advance the cutting-edge technology in XRD materials characterization.

The Ni-rich part of the Al-Ge-Ni phase diagram.

The Ni-rich part of the ternary system Al-Ge-Ni (xNi > 50 at.%) was investigated by means of optical microscopy, powder X-ray diffraction (XRD), differential thermal analysis (DTA) and scanning electron microscopy (SEM). The two isothermal sections at 550 °C and 700 °C were determined. Within these two sections a new ternary phase, designated as τ4, AlyGe9-yNi13±x (hP66, Ga3Ge6Ni13-type) was detected and investigated by single crystal X-ray diffraction. Another ternary low temperature phase, τ5, was found only in the isothermal section at 550 °C around the composition AlGeNi4. This compound was found to crystallise in the Co2Si type structure (oP12, Pnma). The structure was identified by Rietveld refinement of powder data. The NiAs type (B8) phase based on binary Ge3Ni5 revealed an extended solid solubility of Al and the two isotypic compounds AlNi3 and GeNi3 form a complete solid solution. Based on DTA results, six vertical sections at 55, 60, 70, 75 and 80 at.% Ni and at a constant Al:Ni ratio of 1:3 were constructed. Furthermore, the liquidus surface projection and the reaction scheme (Scheil diagram) were completed by combining our results with previous results from the Ni-poor part of the phase diagram. Six invariant ternary reactions were identified in the Ni-rich part of the system.

The Cu-Sn phase diagram, Part I: New experimental results.

Phase diagram investigation of the Cu-Sn system was carried out on twenty Cu-rich samples by thermal analysis (DTA), metallographic methods (EPMA/SEM-EDX) and crystallographic analysis (powder XRD, high temperature powder XRD). One main issue in this work was to investigate the high temperature phases beta (W-type) and gamma (BiF3-type) and to check the phase relations between them. In the high temperature powder XRD experiments the presence of the two-phase-field between the beta- and the gamma-phase could not be confirmed. Detailed study of primary literature together with our experimental results leads to a new phase diagram version with a higher order transformation between these two high temperature phases. The present work is designated as part I of our joint publication. The new findings described here have been included into a completely new thermodynamic assessment of the Cu-Sn phase diagram which is presented in part II.

FFT pattern recognition of crystal HRTEM image with deep learning.

Rapid analysis and processing of large quantities of data obtained from in-situ transmission electron microscope (TEM) experiments can save researchers from the burdensome manual analysis work. The method mentioned in this paper combines deep learning and computer vision technology to realize the rapid automatic processing of end-to-end crystal high-resolution transmission electron microscope (HRTEM) images, which has great potential in assisting TEM image analysis. For the fine-grained result, the HRTEM image is divided into multiple patches by sliding window, and 2D fast Fourier transform (FFT) is performed, and then all FFT images are inputted into the designed LCA-Unet to extract bright spots. LCA-Unet combines local contrast and attention mechanism on the basis of U-net. Even if the bright spots in FFT images are weak, the proposed neural network can extract bright spots effectively. Using computer vision and the information of bright spots above mentioned, the automatic FFT pattern recognition is completed by three steps. First step is to calculate the precise coordinates of the bright spots, the lattice spacings and the inter-plane angles in each patch. Second step is to match the lattice spacing and the angles with the powder diffraction file (PDF) to determine the material phase of each patch. Third step is to merge the patches with same phase. Taking the HRTEM image of zirconium and its oxide nanoparticles as an example, the results obtained by the proposed method are basically consistent with manual identification. Thus the approach could be used to automatically and effectively find the phase region of interest. It takes about 3 s to process a 4 K × 4 K HRTEM image on a modern desktop computer with NVIDIA GPU.

Accurate identification of living Bacillus spores using laser tweezers Raman spectroscopy and deep learning.

Accurately, rapidly, and noninvasively identifying Bacillus spores can greatly contribute to controlling a plenty of infectious diseases. Laser tweezers Raman spectroscopy (LTRS) has confirmed to be a powerful tool for studying Bacillus spores at a single cell level. In this study, we constructed a single-cell Raman spectra dataset of living Bacillus spores and utilized deep learning approach to accurately, nondestructively identify Bacillus spores. The trained convolutional neural network (CNN) could efficiently extract tiny Raman spectra features of five spore species, and provide a prediction accuracy of specie identification as high as 100 %. Moreover, the spectral feature differences in three Raman bands at 660, 826, and 1017 cm-1 were confirmed to mostly contribute to producing such high prediction accuracy. In addition, optimal CNN model was employed to monitor and identify sporulation process at different metabolic phases in one growth cycle. The obtained average prediction accuracy of metabolic phase identification was approximately 88 %. It can be foreseen that, LTRS combined with CNN approach have great potential for accurately identifying spore species and metabolic phases at a single cell level, and can be gradually extended to perform identification for many unculturable bacteria growing in soil, water, and food.

Identification and quantification of Cr, Cu, and As incidental nanomaterials derived from CCA-treated wood in wildland-urban interface fire ashes.

In addition to the combustion of vegetation, fires at the wildland-urban interface (WUI) burn structural materials, including chromated copper arsenate (CCA)-treated wood. This study identifies, quantifies, and characterizes Cr-, Cu-, and As-bearing incidental nanomaterials (INMs) in WUI fire ashes collected from three residential structures suspected to have originated from the combustion of CCA-treated wood. The total elemental concentrations were determined by inductively coupled plasma-time of flight-mass spectrometry (ICP-TOF-MS) following acid digestion. The crystalline phases were determined using transmission electron microscopy (TEM), specifically using electron diffraction and high-resolution imaging. The multi-element single particle composition and size distribution were determined by single particle (SP)-ICP-TOF-MS coupled with agglomerative hierarchical clustering analysis. Chromium, Cu, and As are the dominant elements in the ashes and together account for 93%, 83%, and 24% of the total mass of measured elements in the ash samples. Chromium, Cu, and As phases, analyzed by TEM, most closely match CrO3, CrO2, eskolaite (Cr2O3), CuCrO2, CuCr2O4, CrAs2O6, As2O5, AsO2, claudetite (As2O3, monoclinic), or arsenolite (As2O3, cubic), although a bona fide phase identification for each particle was not always possible. These phases occur predominantly as heteroaggregates. Multi-element single particle analyses demonstrate that Cr occurs as a pure phase (i.e., Cr oxides) as well as in association with other elements (e.g., Cu and As); Cu occurs predominantly in association with Cr and As; and As occurs as As oxides and in association with Cu and Cr. Several Cr, Cu, and As clusters were identified and the molar ratios of Cr/Cu and Cr/As within these clusters are consistent with the crystalline phases identified by TEM as well as their heteroaggregates. These results indicate that WUI fires can lead to significant release of CCA constituents and their combustion-transformed by-products into the surrounding environment. This study also provides a method to identify and track CCA constituents in environmental systems based on multi-element analysis using SP-ICP-TOF-MS.

Phase equilibria and structural investigations in the Ni-poor part of the system Al-Ge-Ni.

The ternary phase diagram Al-Ge-Ni was investigated between 0 and 50 at.% Ni by a combination of differential thermal analysis (DTA), powder- and single-crystal X-ray diffraction (XRD), metallography and electron probe microanalysis (EPMA). Ternary phase equilibria and accurate phase compositions of the equilibrium phases were determined within two partial isothermal sections at 400 and 700 °C, respectively. The two binary intermediate phases AlNi and Al3Ni2 were found to form extended solid solutions with Ge in the ternary. Three new ternary phases were found to exist in the Ni-poor part of the phase diagram which were designated as τ1 (oC24, CoGe2-type), τ2 (at approximately Al67.5Ge18.0Ni14.5) and τ3 (cF12, CaF2-type). The ternary phases show only small homogeneity ranges. While τ1 was investigated by single crystal X-ray diffraction, τ2 and τ3 were identified from their powder diffraction pattern. Ternary phase reactions and melting behaviour were studied by means of DTA. A total number of eleven invariant reactions could be derived from these data, which are one ternary eutectic reaction, six transition reactions, three ternary peritectic reactions and one maximum. Based on the measured DTA values three vertical sections at 10, 20 and 35 at.% Ni were constructed. Additionally, all experimental results were combined to a ternary reaction scheme (Scheil diagram) and a liquidus surface projection.