Phase Identification Research Articles

Automatic breathing phase identification based on the second derivative of the recorded lung sounds

This paper presents a new method for automatic identification of the inspiratory and expiratory breathing phases in lung sound recordings. Adventitious lung sounds (wheezes and crackles), superimposed on the breath sounds, are generally an early indication of the disease, and their timing in the breathing cycle (early/mid/late inspiratory or expiratory) has clinical significance for monitoring or diagnosing disease. Therefore, the identification of the phases of the breathing cycle is an essential step for clinical interpretation of pulmonary auscultation. The proposed algorithm is designed to be robust in the presence of adventitious lung sounds or where the breath sounds may be noisy compared to healthy lung sounds. The algorithm uses the Savitzky & Golay (SG) filter to estimate the second derivative of the lung sound signal then calculates its normalized absolute value. A threshold value is used to clip any large amplitude peaks and, following low-pas filtering, the breathing phases are visible in the plotted signal. A rule-based approach to locating peaks and troughs is then used to identify inspirations and expirations. The performance of the method is evaluated using four different datasets: (a) a longitudinal dataset recorded from 19 subjects with a diagnosis of idiopathic pulmonary fibrosis (IPF), (b) a cross-sectional dataset recorded from 55 subjects who were referred for high-resolution computed tomography (HRCT) scan of the chest for various clinical indications, (c) a longitudinal dataset recorded from 10 healthy subjects, and (d) an open access lung sounds dataset containing recordings from 41 subjects with wheeze (9 with chronic obstructive pulmonary fibrosis and 32 with asthma). On average for inspiratory phase identification the algorithm had a sensitivity of (mean (standard deviation)) 92.84 (12.88)%, positive predictive value of 90.64(15.96)%, and F1-score of 90.67 (12.53)%. For identification of the expiratory phase, the algorithm had an average sensitivity of 92.19 (13.44)%, an average positive predictive value of 91.55 (15.23)%, and an average F1-score of 90.89 (12.45)%. The method shows good potential for automatic identification of breathing phases in recorded lung sounds.

Green synthesis of ZnO nanoparticles and its application for methyl green dye adsorption

This research presents a facile and inexpensive method for synthesizing ZnO nanoparticles using Nauclea latifolia fruit extract as a bioreductant and stabilizer. The prepared particles were characterized using some analytical techniques, including X-ray diffraction (XRD) for crystallinity and phase identification, scanning electron microscopy (SEM) to study surface morphology, Fourier transform infrared (FTIR) spectroscopy for functional groups analysis, transmission electron microscopy (TEM) for grain size analysis, UV–Vis spectroscopy for optical properties, and Brunauer-Emmett-Teller (BET) for surface area analysis. XRD analysis revealed a hexagonal wurtzite structure with an average crystallite size of 14.40 nm. FTIR showed absorption peaks at 3659, 1341, and 460 cm−1, corresponding to hydroxyl, carboxylic, and Zn–O, respectively. SEM image showed an agglomerated surface morphology with a flower-like shape. TEM estimated the particle size range to be 12.54–17.35 nm. UV–Vis scanning showed a broad peak at 373 nm. BET revealed 277.420 m2/g as the specific surface area. A batch adsorption experiment conducted on the performance of the nanoparticles for methyl green (MG) removal from aqueous solution showed highest efficiency of 99.96% at 60 min agitation time and pH of 7, with 0.05 g of the ZnO NPs, confirming the efficiency of the particles. The results of adsorption modelling revealed that the adsorption data were best fit to Freundlich isotherm and general-order kinetic models. Thermodynamic investigation confirmed the adsorption process as spontaneous, feasible, endothermic, and physical. Finally, the simplicity of the synthesis method and the performance evaluation of the ZnO nanoparticles indicate that an efficient and cost-effective adsorbent for MG recovery from aqueous solution has been successfully prepared.

A New X-ray Diffraction Spectrum-Based Untargeted Strategy for Accurately Identifying Ancient Painted Pottery from Various Dynasties and Locations in China

X-ray diffraction (XRD) is extensively used in archaeometric investigation. Herein, we provide a novel XRD spectrum-based untargeted strategy for the classification of ancient painted pottery for various dynasties. It was accomplished using the original spectrum without a phase identification. To eliminate the influence of baseline drift, a new baseline drift correction algorithm was specifically designed for XRD spectra. The algorithm was implemented using local minimum values in the analyzed signal in an iterative optimization manner. The results indicated that with the aid of the algorithm, the baseline drift problem can be successfully resolved, and the classification of ancient painted pottery can be greatly improved. Finally, the developed strategy was successfully used to discriminate ancient painted pottery from the Han and Tang dynasties in the cities of Guyuan and Zhongwei, China. The developed untargeted strategy had the remarkable advantage of almost automatic data analysis. The toolbox of our strategy can be obtained from the authors.

PDF-5+: a comprehensive powder diffraction file™ for materials characterization

For more than 80 years, the scientific community has extensively used International Centre for Diffraction Data's (ICDD®) Powder Diffraction File (PDF®) for material characterization, including powder X-ray diffraction analysis. Historically, PDF was made available for two major material types: one for inorganic analysis and the other for organic analysis. In the early years of the PDF, this two-material approach was implemented due to limited computer capabilities. With Release 2024, ICDD provides a comprehensive database consisting of the entire PDF in one database called PDF-5+, comprised of more than one million entries (1,061,898). The PDF-5+ with a relational database (RDB) construct houses extensive chemical, physical, bibliographic, and crystallographic data, including atomic coordinates and raw data, enabling qualitative and quantitative phase analysis. This wealth of information in one database is advantageous for phase identification, materials characterization, and several data mining applications in materials science. A database of this size needs rigorous data curation and structural and chemical classifications to optimize pattern search/match and characterization methods. Each entry in the PDF has an editorially assigned quality mark. An editorial comment will describe the reason if an entry does not meet the top-quality mark. The editorial processes of ICDD's quality management system are unique in that they are ISO 9001:2015 certified. Among several classifications implemented in PDF-5+, subfiles (such as Bioactive, Pharmaceuticals, Minerals, etc.) directly impact the search/match in minimizing false positives. Scientists with specific field expertise continuously review these subfiles to maintain their quality. This paper describes the features of PDF with an emphasis on the newly released PDF-5+.

Correspondence of the aging behaviors and the phase constituents of the Ti-Mo-based alloys tailored by the Al and Zr additions

Ti-based alloys are promising materials for wide applications, such as aerospace community and biomedical materials. Concerning the applications, the mechanical properties are fundamental and crucial issues. Therefore, effect of the phase constituents on the aging behaviors of the Ti-Mo-Al-Zr alloys has been investigated in this study. The phase constituents of the Ti-Mo-based alloys were manipulated by the introductions of the Al and Zr elements. These metals were chosen since Mo and Al are typical β-stabilizer and α-stabilizer, respectively; while the Zr element is classified as the neutral element. Hence, these three typical addition elements were chosen for tailoring the mechanical properties of the Ti-based alloys making this system a fundamental guideline for further alloy fabrications. The phase identification, microstructure observations, and examinations of micro Vickers hardness were conducted for explaining the mechanical properties of the alloys. The typical ω-phase and α-phase, which often accompany the formation of the β-phase, were focused to clarify the influence of the phase constituents and microstructures on the micro Vickers hardness. A color mapping of micro Vickers hardness along with the recognition of phase constituents explains the alternations of the mechanical properties brought about by the phase transformations.

X-Ray Diffraction and Structural Study of Ba1−xAExTiO3 Ceramics (AE: Ca, Mg and Sr for x=0,0.02,0.04,0.06 and 0.08)

By examining the X-ray powder diffraction data, the microstructure of the BaTiO3 ceramic modified by alkaline earth (AE) metals was characterized. Following profile fitting, the X-ray powder diffraction data of the powder mixtures were analyzed to estimate and evaluate the structural changes of the single-phase AE-modified BaTiO3. Each compound, as well as several polymorphic versions of the same compound, had a unique diffraction pattern, which was a coded representation of the crystal structure. The sole requirement for deciphering this data was the generation of exact data in order to enable phase identification, measurements of lattice parameters, atomic positions, etc. This work compares the various structural characteristics of void species nanostructure using X-ray diffraction with respect to the first sharp diffraction peak (FSDP) of different samples. Various parameters were computed using general expression and a comparison was presented in this paper.

Menstrual Phase Identification Questionnaire (MPIQ): Development and validation of a cross-sectional survey to identify follicular and luteal phases.

Evidence continues to accumulate on the influence of the menstrual phase on several biobehavioral outcomes (e.g., substance misuse). Expansion of this knowledge is limited due to the burdensomeness of accurate menstrual phase assessment. Thus, we sought to create and validate a questionnaire that can be used as a stand-alone item within low-resource settings and numerous study designs (e.g., cross-sectional) to accurately identify both the follicular phase (FP) and the luteal phase (LP). Participants completed the self-administered four-item Menstrual Phase Identification Questionnaire (MPIQ) in two recently completed clinical trials. We assessed the accuracy of two MPIQ scoring criteria (less restrictive and more restrictive), as compared to self-report of onset of menses alone, with progesterone confirmation via dried blood spots. Participants (n = 59) were, on average, 33.7 (standard deviation [SD]: ± 4.3) years old and provided a total of 83 responses. Assessing FP and LP using the self-reported onset of menses alone classified 65.1% of the responses with an overall phase identification accuracy of 60.2%. While the more restrictive MPIQ scoring classified 100% of the responses, it yielded a similar accuracy (68.4%). In contrast, the less restrictive MPIQ scoring classified 100% of the responses and also significantly improved phase identification accuracy to 92.1% (p < .001). The MPIQ, as a stand-alone item, allows all cross-sectional responses to be classified with a high level of accuracy. This low-burden questionnaire can be used alone to identify FP and LP in studies that may be otherwise limited by study design, finances, and/or participant burden. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Increasing thermodynamic stability and electrochemical performance of IT-SOFC cathodes based on Ln2MO4 (Ln = La, Pr; M = Ni, Cu)

La2-xPrxNi1-yCuyO4+δ (x = 0.5, 1, 1.5, y = 0.4, 0.6, 0.8) were synthesized by a citrate-nitrate method and studied as cathode materials for IT-SOFCs by Electrochemical Impedance Spectroscopy (EIS) in symmetrical cells based on the Ce0.8Sm0.2O2-δ (SDC) and La28-zW4+zO54+1.5z (z = 0.85, LWO) electrolytes. Phase identification and crystal structure analysis by means of X-ray powder diffraction (XRPD) shows that solubility of copper y decreases with the increase in praseodymium content x due to increasing structural distortions. Thermodynamic stability of La2-xPrxNi1-yCuyO4+δ at intermediate temperatures in air tends to decrease with praseodymium doping level x and increases with copper substitution for nickel y. Oxides with x = 0.5 and 0.6≤y ≤ 0.8 are shown to be thermodynamically stable at 700 °C in air. Electrochemical performance of the La2-xPrxNi1-yCuyO4+δ electrodes improves with praseodymium and copper doping. The results of EIS measurements at different temperatures and oxygen partial pressures (P(O2)) reveal that the rate-determining step of the overall electrochemical process is charge transfer, which can be a combination of surface diffusion and bulk diffusion. EIS studies in dry and wet air indicate the presence of proton conduction in the studied electrodes. The area-specific resistances (ASRs) for La2-xPrxNi1-yCuyO4+δ with x = 0.5, y = 0.6 and y = 0.8 in contact with SDC are comparable with those reported for Pr2NiO4+δ and equal to 0.07 and 0.065 Ω cm2 at 800 °C.

Interpretable structure-property correlation in X-ray diffraction patterns of HfZrO thin films via machine learning

The X-ray diffraction (XRD) patterns of materials contain important and rich information in terms of structure, strain state, grain size, etc. The XRD can become a powerful fingerprint for material characterizations when it is combined with machine learning techniques. Attempts utilizing machine-learning-based methods mainly focus on phase identification for mixture compounds. Herein, we applied a machine-learning-based method linking XRD patterns of HfZrO thin films directly to their electronic properties in experiments. In accordance with conventional understanding, the machine learning model suggests that non-monoclinic (NM) phases of HfO2 and ZrO2 are among the main contributors to higher relative permittivity and lower leakage current. Furthermore, some minor interfacial phases like TiO x and ZrN x are also proposed to be even more important contributors to our target electronic properties. Our research demonstrates that machine learning has the potential to reveal minor XRD signals from sub-1 nm interfacial layers that have long been considered undetectable and thus ignored by human interpretation.

Identification of Active Phase for Complete Oxidation of Methane on Palladium Surface

Identification of Active Phase for Complete Oxidation of Methane on Palladium Surface

A New Perspective on Thermal Transition in QCD

Abstract Motivated by the picture of partial deconfinement developed in recent years for large-N gauge theories, we propose a new way of analyzing and understanding thermal phase tsuppransition in QCD. We find nontrivial support for our proposal by analyzing the WHOT-QCD collaboration’s lattice configurations for SU(3) QCD in 3 + 1 spacetime dimensions with up, down, and strange quarks. We find that the Polyakov line (the holonomy matrix around a thermal time circle) is governed by the Haar-random distribution at low temperatures. The deviation from the Haar-random distribution at higher temperatures can be measured via the character expansion, or equivalently, via the expectation values of the Polyakov loop defined by the various nontrivial representations of SU(3). We find that the Polyakov loop corresponding to the fundamental representation and loops in the higher representation condense at different temperatures. This suggests that there are three phases, one intermediate phase existing in between the completely-confined and the completely-deconfined phases. Our identification of the intermediate phase is supported also by the condensation of instantons: by studying the instanton numbers of the WHOT-QCD configurations, we find that the instanton condensation occurs for temperature regimes corresponding to what we identify as the completely-confined and intermediate phases, whereas the instantons do not condense in the completely-deconfined phase. Our characterization of confinement based on the Haar-randomness explains why the Polyakov loop is a good observable to distinguish the confinement and the deconfinement phases in QCD despite the absence of the $\mathbb {Z}_3$ center symmetry.

Assessing phosphorus recovery from anaerobic digestion effluent of tapioca starch processing in a pilot – scale fluidized – bed homogeneous crystallizer: Effects of operation modes

This present study aimed to investigate the effects of two crystallization operation modes, batchwise operation (BO) and continuous operation (CO), on P recovery efficiency and characteristics of precipitates in a pilot – scale fluidized – bed homogenous crystallizer, using real anaerobic digestion (AD) effluent derived from tapioca starch processing as influent. Characterization techniques, including SEM – EDX, powder XRD, profile – matching, FT – IR, and crystal size distribution (CSD) analysis, were employed to identify phase compositions. Additionally, the purity and %P content of the target product were quantified using principles of hierarchical equilibrium thermodynamics and elemental analysis. As a result, the highest P recovery efficiency was 72.5 % for BO and 83.2 % for CO at a pH of 9 and a mixing rate of 400 rpm. Phase identification results revealed that struvite formed with Ca – rich solids, such as hydroxyapatite (HAP), notably exhibited higher impurity levels originating from CO modes. Precipitates from BO modes predominantly held 65 – 75 % struvite, with %P content ranging from 7.4 to 13.1 % — surpassing that of single superphosphate fertilizer (SSP, 18 – 22 % as P2O5), resulting in a suitable process in this study. Moreover, results obtained from the cost – benefit analysis (CBA) highlighted that BO scenario had economic viability (NP +0.05 to +1.53) with an estimate production cost of 3.47 USD/kg Premoved. Hence, the findings not only indicated that real AD effluent is suitable for P recovery, yielding high – quality struvite products, but also contribute to understanding the nuanced impact of crystallization modes on recovery yields and characteristics. This insight is invaluable for optimizing the process, leading to environmental benefits through reduced P and nitrogen discharge.

Single-step phase identification and phase locking for coherent beam combination using deep learning

Coherent beam combination offers a solution to the challenges associated with the power handling capacity of individual fibres, however, the combined intensity profile strongly depends on the relative phase of each fibre. Optimal combination necessitates precise control over the phase of each fibre channel, however, determining the required phase compensations is challenging because phase information is typically not available. Additionally, the presence of continuously varying phase noise in fibre laser systems means that a single-step and high-speed correction process is required. In this work, we use a spatial light modulator to demonstrate coherent combination in a seven-beam system. Deep learning is used to identify the relative phase offsets for each beam directly from the combined intensity pattern, allowing real-time correction. Furthermore, we demonstrate that the deep learning agent can calculate the phase corrections needed to achieve user-specified target intensity profiles thus simultaneously achieving both beam combination and beam shaping.

Structural characteristics and properties of heat-resistant nickel β-alloys produced via the centrifugal SHS-casting method

Employing centrifugal self-propagating high-temperature synthesis (SHS) metallurgy, complemented by advanced metallurgical processes such as vacuum induction melting (VIM) and vacuum arc remelting (VAR), yielded the alloy formulation denoted as base–2.5Mo–1.5Re–1.5Ta–0.2Ti. This study investigates the effects of various technological modes and additional metallurgical treatments on the alloy's impurity and non-metallic inclusion content, structural characteristics, mechanical behavior under compression, and its oxidation mechanisms and kinetics when exposed to temperatures of 1150 °C for 30 h. With increasing centrifugal acceleration, the proportion of non-metallic inclusions (number of points) drops from 5 to 1–2 points. The best combination mechanical properties, including σucs = 1640 ± 20 MPa, σys = 1518 ± 10 MPa, and residual deformation were observed in alloys processed under conditions of increased gravitational force (g = 50). Within a centrifugal force range of g = 20÷300, the composition of the synthesis products aligned with the theoretical expectations. The total content of impurities is 0.15 ± 0.02 %, with a decrease in gas impurities–oxygen and nitrogen levels reduced to 0.018 % and 0.0011 %, respectively. The structural analysis of the alloys revealed the presence of globular and streaked inclusions of a chromium-based solid solution embedded within the matrix. Inclusions with thickness of 2–8 μm are present in the intergranular space: (Cr)Ni,Mo,Co, (Cr)Mo,Re and (Cr)Re,Mo. The formation of the Ni(Al,Ti) phase at grain boundaries was identified, contributing to an enhancement in plastic resistance and overall strength of the alloy. Oxidation mechanisms varied across different processing modes, with the size of structural components significantly influencing oxidation kinetics. The weight gain observed in SHS samples was 70 ± 10 g/m2 with oxidation predominantly occurring along the NiAl interphase boundaries and penetrating into the depth of the sample. TEM facilitated the identification of phases enriched with Ti microadditions, reducing the levels of dissolved nitrogen and oxygen within the intermetallic phase to a combined weight percentage (ΣO,N) of 0.0223 wt.%.

Recent Progress and Challenges in Controlling Secondary Phases in Kesterite CZT(S/Se) Thin Films: A Critical Review

Kesterite-based copper zinc tin sulfide (CZTS) and copper zinc tin selenide (CZTSe) thin films have attracted considerable attention as promising materials for sustainable and cost-effective thin-film solar cells. However, the successful integration of these materials into photovoltaic devices is hindered by the coexistence of secondary phases, which can significantly affect device performance and stability. This review article provides a comprehensive overview of recent progress and challenges in controlling secondary phases in kesterite CZTS and CZTSe thin films. Drawing from relevant studies, we discuss state-of-the-art strategies and techniques employed to mitigate the formation of secondary phases. These include a range of deposition methods, such as electrodeposition, sol-gel, spray pyrolysis, evaporation, pulsed laser deposition, and sputtering, each presenting distinct benefits in enhancing phase purity. This study highlights the importance of employing various characterization techniques, such as X-ray diffraction, Raman spectroscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy, for the precise identification of secondary phases in CZTS and CZTSe thin films. Furthermore, the review discusses innovative strategies and techniques aimed at mitigating the occurrence of secondary phases, including process optimization, compositional tuning, and post-deposition treatments. These approaches offer promising avenues for enhancing the purity and performance of kesterite-based thin-film solar cells. Challenges and open questions in this field are addressed, and potential future research directions are proposed. By comprehensively analyzing recent advancements, this review contributes to a deeper understanding of secondary phase-related issues in kesterite CZT(S/Se) thin films, paving the way for enhanced performance and commercial viability of thin-film solar cell technologies.

Rare earth Gd3+-substituted Co spinel nanoferrites structural, dielectric and magnetic properties

Gadolinium-doped cobalt ferrite CoGdxFe2-xO4 (CGF) were produced using citrate gel auto-combustion method (x = 0–0.025 at an interval of 0.005). An investigations on the impact of Gd3+ ions on structural, dielectric, and magnetic properties were conducted. The synthesized materials were characterized using the following techniques: X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, ultraviolet visible spectroscopy (UV), impedance (dielectric) analysis, and vibrating sample magnetometer (VSM). The crystalline size ranging from 48 to 13 nm. The Williamson-Hall plots were utilized to examine the impact of crystalline size and lattice strain on the peak broadening of each sample. The X-ray diffraction spectra were used to confirm phase identification and spinel structure. The results of FTIR examinations show that strong metal oxide bonds can be found in the tetrahedral and octahedral sites at wavelengths of 351–376 cm−1 and 567–574 cm−1, respectively. The obtained samples to study optical energy bandgap (Eg), which vary from 2.01 eV to 2.45 eV and show that they are semiconducting materials.The Maxwell-Wagner model and Koop's theory have both been used to study the behaviour of dielectric properties. The dielectric constant at room temperature to 673 K in the frequency range 50 Hz–1 MHz to study the dielectric constant, dielectric loss, AC electrical conductivity, and impedance. The impedance charts show how conductivity was impacted by grain and grain boundary mechanisms. In relation to an applied field of 100 kOe, magnetization was measured at 3 K and 300 K. The blocking temperature (TB), which exhibits super-paramagnetic behaviour, is found to be approximately 359 K according to zero-field cooling ZFC and field cooling. The FC experiments carried out in the temperature range of 3 K–360 K under an applied field of 100 Oe.

Investigation of Structural, Dielectric, Magnetic and Impedance Spectroscopy of MgO/CuFe2O4 Nanocomposites

This study investigates the properties of (MgO)x/(CuFe2O4)1-x x= 10–50 wt.% nanocomposites (NCPs) prepared by physical mixing of both materials. The crystal structure, phase identification and morphology were analyzed by x-ray Diffraction (XRD) and Scanning Electron Microscope (SEM). The investigation revealed the formation of required phases which are in nanometer dimension (22–54 nm) computed by Debye-Sherrer’s formula. Both the real and imaginary parts of dielectric constant (ε/ &amp; ε//), a.c. conductivity (σac), and impedance were measured in the frequency range of 1kHz to 2MHz by LCR meter. The ε/ and ε// revealed a decreasing trend with frequency, while MgO weight fractions in NCPs enhance the permittivity values. The real and imaginary parts of impedance (Z/ &amp; Z//) demonstrate a decreasing trend with frequency which is ascribed to increase in σac. The complex impedance spectroscopy (CIS) analysis shows semicircular arcs at higher frequency, which are due to electrical transport properties of conducting grains. The effect of MgO contents on magnetic properties were analyzed by measuring M-H loops at room temperature through vibrating sample magnetometer (VSM). Both the saturation magnetization (Ms) and coercivity (Hc) values show a decreasing profile with MgO fractions, which are due to increase in nonmagnetic contents and decrease in surface anisotropy respectively.

The application of “transfer learning” in optical microscopy: the petrographic classification of metallic minerals

Abstract Analysis of optical microscopic image data is crucial for the identification and characterization of mineral phases, and thus directly relevant to the subsequent methodology selections of further detailed petrological exploration. Here we present a novel application of Swin Transformer, a deep learning algorithm to classify metal mineral phases such as arsenopyrite, chalcopyrite, gold, pyrite, and stibnite, in images captured by optical microscopy. To speed up the training process and improve the generalization capabilities of the investigated model, we adopt the “transfer learning” paradigm by pretraining the algorithm using a large, general-purpose, image dataset named ImageNet-1k. Further, we compare the performances of the Swin Transformer with those of two well-established Convolutional Neural Networks (CNNs) named MobileNetv2 and ResNet50, respectively. Our results highlight a maximum accuracy of 0.92 for the Swin Transformer, outperforming the CNNs. To provide an interpretation of the trained models, we apply the so-called Class Activation Map (CAM), which points to a strong global feature extraction ability of the Swin Transformer metal mineral classifier that focuses on distinctive (e.g., colors) and microstructural (e.g., edge shapes) features. The results demonstrate that the deep learning approach can accurately extract all available attributes, which reveals the potential to assist in data exploration and provides an opportunity to carry out spatial quantization at a large scale (cm-mm). Simultaneously, boosting the learning processes with pre-trained weights can accurately capture relevant attributes in mineral classification, revealing the potential for application in mineralogy and petrology, as well as enabling its use in resource explorations.

Chitosan-assisted self-assembly of flower-shaped ε-Fe2O3 nanoparticles on screen-printed carbon electrode for Impedimetric detection of Cd2+, Pb2+, and Hg2+ heavy metal ions in various water samples

This study focuses on the fabrication of a novel sensing platform on a screen-printed carbon electrode, modified by a combination of hydrothermally synthesized iron dioxide (ε-Fe2O3) nanoparticles and Chitosan (CS) biopolymer. This unique organic-inorganic hybrid material was developed for Electrochemical Impedance Spectroscopy (EIS) sensing, specifically targeting heavy metal ions that include Hg2+, Cd2+, as well as Pb2+. The investigation encompassed a comprehensive analysis of various aspects of the prepared Fe2O3 and CS/ε-Fe2O3 nanocomposites, including phase identification, determination of crystallite size, assessment of surface morphology, etc. CS/ε-Fe2O3 was drop-casted and deposited on the Screen-Printed Electrode (SPE). The resulting sensor exhibited excellent performance in the precise and selective quantification of Hg2+, Cd2+, and Pb2+ ions, with minimal interference from other substances. The fabricated sensor exhibits excellent performance as the detection range for Hg2+, Cd2+, and Pb2+ ions linearity is 2–20 μM, sensitivity, and LOD are 243 Ω/ μM cm2 and 0.191 μM, 191 Ω/μM cm2, and 0.167 μM, 879 Ω/ μM cm2, and 0.177 μM respectively. The stability of the CS/ε-Fe2O3/SPE electrode is demonstrated by checking its conductivity for up to 60 days for Hg2+, Cd2+, and Pb2+ ions. The reusability of the fabricated electrode is 14 scans, 13 scans, and 12 scans for Hg2+, Cd2+, and Pb2+ ions respectively. The findings indicate the successful development of an innovative CS/ε-Fe2O3 electrode for the EIS sensing platform. This platform demonstrates notable potential for addressing the critical need for efficient and sensitive EIS sensors capable of detecting a range of hazardous heavy metal ions, including Hg2+, Cd2+, and Pb2+.

Er2FeCrO6: Emerging efficient nanomaterials for multiferroics

The present work deals with the rare earth Erbium-based double perovskite. The chemical precipitation route was adopted followed by calcination at 700 °C for the synthesis of Er2FeCrO6 (EFCO). The phase identification was performed using Rietveld refinement. Morphological analysis shows the formation of nanoparticles of EFCO having an average size of ∼15 nm. The M − H curve recorded at 10 K shows the weak ferromagnetic nature of the sample. Temperature variation of magnetization illustrates that EFCO nanoparticles undergo ferromagnetic transition at Tc = 14 K. Temperature-dependent P-E studies display ferroelectric behaviour with a ferroelectric transition temperature (TCE = 453 K) which is beyond room temperature. The simultaneous existence of magnetic and electric orders may introduce EFCO as a new multiferroic oxide and a good alternative to the popular compound BiFeO3.