Phase Identification Research Articles

Improved topology identification for distribution network with relatively balanced power supplies

AbstractHaving correct distribution network topology information is essential for system state estimation, line loss analysis, electricity theft detection and fault location. At present, with continuous deployment of smart sensors, a large amount of monitoring data is collected, which enables refined management for distribution network. A data‐driven low voltage (LV) distribution network topology identification method is proposed, which realises transformer‐customer pairing and customer phase identification for distribution network with relatively balanced power supplies. Firstly, an integrated similarity coefficient of voltage curve is proposed, which can reflect the neighbourhood relationship within stations while increase the distinction between stations; the K‐Nearest Neighbour (KNN) algorithm is used to propagate the service transformer labels to complete transformer‐customer association. Then, the influence of power fluctuation on voltage curve is analysed and a dynamic sliding window model is adopted to search for voltage segments with significantly difference among three phase feeders to formulate a voltage time series to identify customer phase. Finally, the results are corrected and verified based on the principle of network power balance. The proposed algorithm is tested in two different real substations in China and Europe and shows high accuracy and robustness especially in distribution network with relatively balanced power supplies.

Investigation of structural, electrical and thermoelectric properties of cobalt-zinc ferrites/graphene nanocomposite

The present work was related to synthesis of graphene (0, 1.25 and 2.5 %) incorporation in cobalt-zinc ferrites by using sol–gel auto combustions method. The face center cubic (FCC) crystal structure and crystallite size varies from 16 to 18 nm was examined by XRD analysis. While as the irregular, non-uniform and flake like surface morphology of cobalt zinc/graphene nanocomposite (Co-Zn-G NCs) were observed via SEM images. In addition, the phase identification and presence of different functional groups such as M−O (metal oxide), OH, C-C, C = O and C = C were investigated by Raman spectroscopy and FTIR analyses. Further, I-V two probe method was used to observed the resistivity decrease (7.5x 108 to 2.5x 106 ohm-cm) and conductivity increased (1.2x10-9 to 3.8x10-7S/cm) of Co-Zn-G NCs. After that the thermoelectric behavior of Co-Zn-G NCs indicated that due to increase the temperature then seebeck coefficient decrease as vice versa. The overall result shows that the 2.5 % graphene incorporation in Co-Zn ferrites expressed the excellent electrical and thermoelectric properties. In future the Co-Zn-G nanocomposite used for the development of temperature sensor, reduce eddy current loss and spintronic devices.

Mineralogical study of phosphate rocks by quantitative rietveld refinement

Phosphorites from the Kodjari deposits of Burkina Faso are composite rocks with a fine-grained matrix phase and dispersed larger grains. Chemical analysis by XRF revealed a CaO content of 35.98–41.33 wt% and a P2O5 content of 26.85–30.75 wt%. X-ray diffraction patterns show the abundant presence of carbonate-fluorapatite (Francolite), Hydroxyapatite and Quartz, with the minor phases Wavellite, Maricite and phyllosilicates. The mineralogical compositions by Quantitative Rietveld Analysis shows the presence of 75.41–83.85 wt% of phosphate phases, with carbonate structural groups in Francolite and Hydroxyapatite, while F ions are in the Francolite structure. FTIR analyses also validates the identification of mineral phases with the observation of bands from structural phosphate groups, from carbonate groups and from structural F. This study highlights the effectiveness of Quantitative Rietveld Analysis in quantifying the mineralogical composition of phosphorites. The method is suitable for monitoring the exploitation of mineral deposits.

Phase Identification and Discovery of an Elusive Polymorph of Drug‐Polymer Inclusion Complex Using Automated 3D Electron Diffraction

Abstract3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low‐dose and low‐bias 3D ED protocol has been implemented to identify six phases from a multiple‐phase melt‐crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low‐dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF‐PEG IC‐I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF‐PEG IC‐II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF‐PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam‐sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.

Phase Identification and Discovery of an Elusive Polymorph of Drug-Polymer Inclusion Complex Using Automated 3D Electron Diffraction.

3D electron diffraction (3D ED) has shown great potential in crystal structure determination in materials, small organic molecules, and macromolecules. In this work, an automated, low-dose and low-bias 3D ED protocol has been implemented to identify six phases from a multiple-phase melt-crystallisation product of an active pharmaceutical ingredient, griseofulvin (GSF). Batch data collection under low-dose conditions using a widely available commercial software was combined with automated data analysis to collect and process over 230 datasets in three days. Accurate unit cell parameters obtained from 3D ED data allowed direct phase identification of GSF Forms III, I and the known GSF inclusion complex (IC) with polyethylene glycol (PEG) (GSF-PEG IC-I), as well as three minor phases, namely GSF Forms II, V and an elusive new phase, GSF-PEG IC-II. Their structures were then directly determined by 3D ED. Furthermore, we reveal how the stabilities of the two GSF-PEG IC polymorphs are closely related to their crystal structures. These results demonstrate the power of automated 3D ED for accurate phase identification and direct structure determination of complex, beam-sensitive crystallisation products, which is significant for drug development where solid form screening is crucial for the overall efficacy of the drug product.

Cements and concretes materials characterisation using machine-learning-based reconstruction and 3D quantitative mineralogy via X-ray microscopy.

3D imaging via X-ray microscopy (XRM), a form of tomography, is revolutionising materials characterisation. Nondestructive imaging to classify grains, particles, interfaces and pores at various scales is imperative for our understanding of the composition, structure, and failure of building materials. Various workflows now exist to maximise data collection and to push the boundaries of what has been achieved before, either from singular instruments, software or combinations through multimodal correlative microscopy. An evolving area on interest is the XRM data acquisition and data processing workflow; of particular importance is the improvement of the data acquisition process of samples that are challenging to image, usually because of their size, density (atomic number) and/or the resolution they need to be imaged at. Modern advances include deep/machine learning and AI resolutions for this problem, which address artefact detection during data reconstruction, provide advanced denoising, improved quantification of features, upscaling of data/images, and increased throughput, with the goal to enhance segmentation and visualisation during postprocessing leading to better characterisation of samples. Here, we apply three AI and machine-learning-based reconstruction approaches to cements and concretes to assist with image improvement, faster throughput of samples, upscaling of data, and quantitative phase identification in 3D. We show that by applying advanced machine learning reconstruction approaches, it is possible to (i) vastly improve the scan quality and increase throughput of 'thick' cores of cements/concretes through enhanced contrast and denoising using DeepRecon Pro, (ii) upscale data to larger fields of view using DeepScout and (iii) use quantitative automated mineralogy to spatially characterise and quantify the mineralogical/phase components in 3D using Mineralogic 3D. These approaches significantly improve the quality of collected XRM data, resolve features not previously accessible, and streamline scanning and reconstruction processes for greater throughput.

Paste, aggregate, or air? That is the question.

The Ambassador Bridge between Detroit, Michigan, and Windsor, Ontario, has served for almost 100 years as North America's busiest international border crossing. But in 2025, the Ambassador will be replaced by the new Gordie Howe International Bridge. The Gordie Howe is a cable-stayed bridge, with two massive 220 m tall concrete piers on opposite banks of the St. Claire River, a single clear span of 853 m, and 42 m of clearance over this busy waterway. To ensure durability in this harsh freeze-thaw environment, air-entrained concrete is specified throughout. And, to ensure the quality of air entrainment, the ASTM C 457 Procedure C, Contrast Enhanced Method is employed. While a similar automated microscopic approach has been in use for well over a decade according to EN 480-11 Determination of air void characteristics in hardened concrete, this is the first large-scale application of automated air void assessment in North American infrastructure. According to the ASTM Procedure C, the air void characteristics are determined through digital image processing, while the paste content may be determined by either mix design parameters, manual point count, or 'other means'. Of these three options, point counting is used for Gordie Howe; but in parallel, during each point count, the digital image coordinates and phase identifications for each evaluated stop are recorded. This allows for training of a neural network, for automated determination of paste content, as demonstrated here.

Role OF KIT-6 on the fungicide and pesticide activities of zinc, copper and magnesium oxide nanoparticles prepared using Camellia sinensis extract (tea plant) through green synthesis

Nanoparticles have contributed to the society in many ways. They are of great use in various fields, such as semiconductors, medicine, cosmetics, agriculture, electronics, automobiles, catalysis, biosensors, paints etc. Magnesium, copper and zinc oxide nanoparticles have been synthesized from Camellia sinensis (tea) leaf extract in an environmentally friendly way. Surface morphology, phase identification and elemental analysis of the prepared metal oxide nanoparticles were carried out by SEM, powder XRD and EDAX analyses, respectively. The prepared metal oxide nanoparticles were tested for their fungicidal activity against the fungi pestalotiopsis theae, which causes grey blight disease, and pesticide activity against the pest oligonychus coffeae, which causes red spider mites disease in plants, particularly in tea plants. The effect of mesoporous KIT-6 on the pesticide activity of the prepared nanoparticles was also studied. The increased pesticidal property of the nanoparticles is correlated with the meso structures and properties of KIT-6. The bio efficacy of these metal oxide nanoparticles against the selected fungus and pest is discussed and compared in detail.

Immobilisation of chromium in magnesium carbonate minerals

AbstractHexavalent chromium (Cr6+) is a toxic carcinogenic pollutant that might be released by the mining and processing of ultramafic rocks and nickel laterites and which requires permanent removal from the contaminated biosphere. Ultramafic material can also serve as a feedstock for the sequestration of CO2 resulting from the growth of new minerals, raising the intriguing proposition of integrated sequestration of both pollutants, CO2 and chromium, into magnesium carbonates. Such a synergistic process downstream of ore recovery and mineral processing could be an elegant proposition for more sustainable utilisation and management of the Earth's resources. We have therefore carried out an experimental and microanalytical study to investigate potentially suitable carbonate minerals. Uptake of chromium in carbonate phases was determined, followed by identification of the crystalline phases and characterisation of the local structural environment around the incorporated chromium centres. The results suggest that neither nesquehonite nor hydromagnesite have the structural capacity to incorporate Cr6+ or Cr3+ significantly at room temperature. We therefore propose that further research into this technology should focus on laboratory assessments of other phases, such as layered double hyroxides, that have a natural structural capacity to uptake both chromium and CO2.

Dynamic diffusive interfacial transport (D-DIT): A novel quantitative swelling technique for developing binary phase diagrams of aqueous surfactant systems.

Current methods to develop surfactant phase diagrams are time-intensive and fail to capture the kinetics of phase evolution. Here, the design and performance of a quantitative swelling technique to study the dynamic phase behavior of surfactants are described. The instrument combines cross-polarized optical and short-wave infrared imaging to enable high-resolution, high-throughput, and in situ identification of phases and water compositions. Data across the entire composition spectrum for the dynamics and phase evolution of a binary aqueous non-ionic surfactant solution at two isotherms are presented. This instrument provides pathways to develop non-equilibrium phase diagrams of surfactant systems-critical to predicting the outcomes of formulation and processing. It can be applied to study time-dependent material relationships across a diverse range of materials and processes, including the dissolution of surfactant droplets and the drying of aqueous polymer films.

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

To bolster the accuracy of existing methods for automated phase identification from X-ray diffraction (XRD) patterns, we introduce a machine learning approach that uses a dual representation whereby XRD patterns are augmented with simulated pair distribution functions (PDFs). A convolutional neural network is trained directly on XRD patterns calculated using physics-informed data augmentation, which accounts for experimental artifacts such as lattice strain and crystallographic texture. A second network is trained on PDFs generated via Fourier transform of the augmented XRD patterns. At inference, these networks classify unknown samples by aggregating their predictions in a confidence-weighted sum. We show that such an integrated approach to phase identification provides enhanced accuracy by leveraging the benefits of each model’s input representation. Whereas networks trained on XRD patterns provide a reciprocal space representation and can effectively distinguish large diffraction peaks in multi-phase samples, networks trained on PDFs provide a real space representation and perform better when peaks with low intensity become important. These findings underscore the importance of using diverse input representations for machine learning models in materials science and point to new avenues for automating multi-modal characterization.

Influence of Cu Addition on the Wear Behavior of a Eutectic Al–12.6Si Alloy Developed by the Spray Forming Method

In the present study, the influence of the addition of copper (Cu) on the wear behavior of a Al-12.6Si eutectic alloy developed using the spray forming (SF) method was discussed, and the results were compared with those of as-cast (AC) alloys. The microstructural features of the alloys were examined using both optical and the scanning electron microscopy, and the chemical composition and phase identification were achieved by X-ray diffraction (XRD) analysis. The results revealed that the microstructure of binary the SF alloy consisted of fine primary and eutectic Si phases, evenly distributed in the equiaxed α-Al matrix, whereas the Cu-based SF ternary alloy consisted of uniformly distributed fine eutectic Si particulates and spherical-shaped θ-Al2Cu precipitates, uniformly distributed in α-Al matrix. In contrast, the AC ternary (Al-12.6Si-2Cu) alloy consisted of unevenly dispersed eutectic Si needles and the coarse intermetallic compound θ-Al2Cu in the α-Al matrix. The addition of Cu enhanced the micro hardness of the SF ternary alloy by 8, 34, and 41% compared to that of the SF binary, AC ternary, and binary alloys, respectively. The wear test was conducted using a pin-on-disc wear testing machine at different loads (10–40 N) and sliding velocities (1–3 ms−1). The wear tests revealed that SF alloys exhibited an improved wear behavior in the entire applied load and sliding velocity range in comparison to that of the AC alloys. At a load of 40 N and a sliding velocity of 1 ms−1, the wear rate of the SF2 alloy is 62, 47, and 23% lower than that of the AC1, AC2, and SF1 alloys, respectively. Similarly, at a sliding velocity of 3 ms−1, the wear rate of the SF2 alloy is 52%, 42%, and 21% lower than that of the AC1, AC2, and SF1 alloys, respectively. The low wear rate in the SF2 alloy was due to the microstructural modification during spray forming, the precipitation of fine Al2Cu intermetallic compounds, and increased solid solubility. The SF alloys show an increased transition from oxidative to abrasive wear, while the AC alloys demonstrate wear mechanisms that change from oxidative to abrasive, including delamination, with an increase in sliding velocity and load.

Effect of signal‐to‐noise ratio on the automatic clustering of X‐ray diffraction patterns from combinatorial libraries

AbstractHierarchical clustering algorithm has been applied to identify the X‐ray diffraction (XRD) patterns from a high‐throughput characterization of the combinatorial materials chips. As data quality is usually correlated with acquisition time, it is important to study the hierarchical clustering performance as a function of data quality in order to optimize the efficiency of high‐throughput experiments. This work investigated the effects of signal‐to‐noise ratio on the performance of hierarchical clustering using 29 distance metrics for the XRD patterns from Fe−Co−Ni ternary combinatorial materials chip. It is found that the clustering accuracies evaluated by the F1 score only fluctuate slightly with signal‐to‐noise ratio varying from 15.5 to 22.3 (dB) under the experimental condition. This suggests that although it may take 40–50 s to collect a visually high‐quality diffraction pattern, the measurement time could be significantly reduced to as low as 4 s without substantial loss in phase identification accuracy by hierarchical clustering. Among the 29 distance metrics, Pearson χ2 shows the highest mean F1 score of 0.77 and lowest standard deviation of 0.008. It shows that the distance matrixes calculated by Pearson χ2 are mainly controlled by the XRD peak shifting characteristics and visualized by the metric multidimensional data scaling.

Synergistic effects of Core@Shell (CZTS@CZTSe) on photovoltaic & photocathode applications

Nanotechnology has undergone significant advancements attributable to the synthesis of nanoparticles (NPs), reflecting the collaborative endeavours of numerous researchers. This investigation contributes to the field by fabricating core–shell (Csh) NPs composed of CZTS (Cu2ZnSnS4)-CZTSe(Cu2ZnSn(SSe)4), exhibiting potential applications as photocatalysts for water treatment and photocathodes for solar cells. Utilizing the innovative “Hot-Injection” method in NP synthesis represents a noteworthy breakthrough, facilitating a reduction in reaction time and enabling precise control over morphology. Characterization employing diverse techniques has unveiled noteworthy structural and electrochemical properties. This includes the identification of a Kesterite phase with varying S and Se ratios, as well as binary/ternary phases influencing purity. UV–vis absorbance spectroscopy has provided insights into electronic properties, while electrochemical impedance spectroscopy has indicated the low charge transfer resistance (Rct) of the CZTS80Se20 core–shell structure, positioning it as a prime candidate for application procedures. Photocatalytic efficiency assessment involved subjecting the NPs to Methylene blue dye under visible light, resulting in a maximum efficiency of 99.9% within a certain range of catalyst concentrations. This study also demonstrated the NP’s suitability as absorber layers of optimal thickness in solar cells, deposited through the Electrophoretic Deposition (EPD) technique. Linear and symmetric I-V response results indicate the ohmic nature of the contacts. This research represents a significant stride forward, unveiling promising prospects for the utilisation of core–shell CZTS-CZTSe NPs across.

Effect of alloying elements on microstructural and electrical property in Sn–Cu–Bi lead-free solder alloys

Sn–0.7Cu eutectic alloy is a low-cost alloy with moderate melting point and wetting that appears to be adequate for some applications. Lead-free Sn–0.7Cu joint solders on copper substrate were investigated in this study. To enhance the performance of the alloy, many previous studies verified the importance of adding bismuth to the eutectic alloy. (84.3Sn–0.7Cu–15Bi) ternary alloy is regarded as a potential substitute for hazardous Sn–Pb solders, possessing a melting point of 206 °C. The effects of incorporating selenium, zinc, or indium on the electrical characteristics, crystal structure and microstructure of the tin copper-bismuth alloy were examined. Phase identification of the base solder and various newly developed alloys was performed by X-ray diffraction tests and an LCR meter was used to measure the electrical resistance of the prepared samples. The unit cell volume, and lattice parameters of the tetragonal Sn base were altered, along with the electrical characteristics of the base solder alloy. According to the test results, the best addition was by indium since it reduced the grain size of the base solder alloy. Smaller grain size improves the mechanical properties of the solder. Furthermore, indium addition increased the electrical conductivity of the base solder alloy compared to other alloying additions.

Oxidation of Fe35Mn21Ni20Cr12Al12 High Entropy Alloy in Dry Air

The isothermal oxidation of a Fe35Mn21Ni20Cr12Al12 high entropy alloy (HEA) was investigated in dry air for 50 h at 500, 600, and 700 °C after 90% cold rolling. The Fe35Mn21Ni20Cr12Al12 HEA behaves according to the linear oxidation rate with rate constants of 1 × 10−6, 3 × 10−6, and 7 × 10−6 g/(cm2·s) for oxidation at 500 °C, 600 °C, and 700 °C, respectively. The activation energy for oxidation of the HEA was calculated to be 60.866 KJ/mole in the 500–700 °C temperature range. The surface morphology and phase identification of the oxide layers were characterized. The formation of MnO2, Mn2O3, Mn3O4, Cr2O3, and Al2O3 in the oxide layers along with Fe2O3 is the key to the oxidation mechanism. The elemental mapping and line EDX scans were performed to identify the oxidation mechanisms.

Fabricating a 3D floating porous PDMS − Ag/AgBr decorated g-C3N4 nanocomposite sponge as a re-usable visible light photocatalyst

In this study, polymeric graphitic carbon nitride (g-C3N4) semiconductors was synthesized via a thermal condensation method. Subsequently, Ag/AgBr nanoparticles with varying ratios were decorated onto the g-C3N4 surface using the water/oil emulsion method. The resulting nanocomposites were characterized using XRD for phase identification and structural analysis, HR-TEM and SEM&EDAX for morphological structure, particle size, and elemental composition analysis, and XPS for investigating the chemical state and electronic structure. The impact of Ag/AgBr content on the optical properties of g-C3N4 were also studied such as (optical bandgap (Eg), refractive index (n), extinction coefficient (k), optical conductivity (σopt) and dielectric function (ε*)), Electrochemical impedance spectroscopy (EIS), PL spectroscopy and Chrono-amperometric investigations were conducted to assess the charge transfer capabilities and long-term durability of the prepared nanocomposites. The results revealed a reduction in Ag/AgBr particle size with an increase in g-C3N4 content, accompanied by a decrease in the optical bandgap from 2.444 eV to 2.393 eV. Furthermore, the nanocomposites exhibited enhanced degradation efficiencies of RhB dye, with the highest tested content of Ag/AgBr achieving 100% degradation after 120 min of irradiation. However, the challenge of catalyst separation after the degradation process remained. To address this issue, we developed a novel approach by impregnating Ag/AgBr@g-C3N4 photocatalyst onto a floating porous sponge using a simple sugar-template technique, offering potential as a reusable photocatalyst material. Furthermore, the 3D PDMS − Ag/AgBr@g-C3N4 photocatalyst was evaluated and found to maintain nearly the same photocatalytic efficiency for up to 5 consecutive cycles.

Effects of induction furnace slag on the durability properties of concrete

In this study, three major properties of concrete were compared, viz: water absorption, compressive strength and chloride ion penetration. The percentage amounts of Induction furnace slag (IFS) varied in concrete to replace cement were 0%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55% and 60%. These specimens were made to undergo curing for 7, 14, 28, 56 and 90 days following which the aforementioned tests took place. The results of the three tests (i.e. compressive strength test, water absorption test and chloride ion penetration test) showed an optimal performance of concrete at 25% IFS replacement. Some of the specimens containing varying amounts of slag (i.e. 0%, 25% and 45%) were subjected to microstructural and phase identification tests in other to establish their distinguishing properties. These tests include Scanning Electron Microscopy (SEM) and X-ray Diffraction (XRD). Meanwhile the IFS was also examined for its composition through X-ray Fluorescence (XRF). Predictive models were generated from all the results obtained. The models developed can predict the compressive strength of concrete (Y) if information on the percentage of IFS replaced (X1), the depth of chloride ion penetration into concrete (X2) and the amount of void left in concrete after water is absorbed (X3) are all known. There existed a high correlation; strong relationship between compressive strength and the depth of chloride ion penetration; and between compressive strength and the percentage volume of void.

Defect-induced improved structural and electrical properties of Gd-Sr co-doped ceria for solid oxide fuel cell applications

Compositions of Ce1-x-yGdxSryO2-δ (x = 0.1, 0.2, and y = 0.025, 0.05, 0.075) are prepared using the sol-gel auto combustion method. The as-prepared powders are calcined at 500 °C for 2 h, followed by pelletization and sintering at 1450 °C for 4 h. Phase identification, defect distribution, microstructural analysis, and conductivity are examined using X-ray diffraction, Raman spectroscopy, scanning electron microscopy and impedance spectroscopy, respectively. A single crystalline phase is formed, and the lattice parameter variation with dopant concentration exhibits anomalous behavior. All the samples show relative densities higher than 92 %. The partial liquid phase sintering is also observed. In co-doped samples, the doping of 10 % Gd with 2.5 % Sr exhibits the highest conductivity, i.e., 1.20 × 10−2 S cm−1 at 600 °C. The developed materials are used in anode-supported cell testing. It shows a maximum power density of 112.7 mW cm−2 and a current density of 288.7 mA cm−2 at 600 °C with wet hydrogen and air on the anode and cathode side, respectively.

Chromatin structure from high resolution microscopy: Scaling laws and microphase separation.

Recent advances in experimental fluorescence microscopy allow high accuracy determination (resolution of 50nm) of the three-dimensional physical location of multiple (up to ∼10^{2}) tagged regions of the chromosome. We investigate publicly available microscopy data for two loci of the human Chr21 obtained from multiplexed fluorescence in situ hybridization (FISH) methods for different cell lines and treatments. Inspired by polymer physics models, our analysis centers around distance distributions between different tags with the aim being to unravel the chromatin conformational arrangements. We show that for any specific genomic site, there are (at least) two different conformational arrangements of chromatin, implying coexisting distinct topologies which we refer to as phase α and phase β. These two phases show different scaling behaviors: the former is consistent with a crumpled globule, while the latter indicates a confined, but more extended conformation, such as a looped domain. The identification of these distinct phases sheds light on the coexistence of multiple chromatin topologies and provides insights into the effects of cellular context and/or treatments on chromatin structure.