Room-Temperature Superparamagnetic FeCu Nanoalloys: Insights into Magnetic Behavior from Synthesis and Simulation

Bimetallic nanoalloys combining magnetic and noble metals are promising for magnetic sensors, catalysis, optical detection, and biomedical imaging applications. Their development relies on understanding morphology, electronic structure, and crystallography. This study explores iron-based magnetic nanoalloys using efficient synthesis and advanced characterization. Molecular dynamics (MD) simulations examined atomic-scale morphology and structural features, linking them to magnetic behavior. A spin-lattice dynamics algorithm simulated iron–copper (FeCu) nanoalloys of varying sizes and compositions. FeCu nanoalloys were synthesized via a one-step reduction reaction and analyzed using multiple techniques, yielding nanoparticles with high saturation magnetization and an 11 nm average size. Simulations and experiments confirmed core-shell (CS) and Janus morphologies, where copper shells an iron core. Findings suggest that composition, rather than morphology alone, predominantly influences magnetic properties, while the core-shell morphology enhances oxidation resistance due to the noble copper metal employed. This study is the first to integrate the spin-lattice algorithm with experimental analysis, providing consistent insights into design and accurate characterization. Thus, it confirms the practical and novel synthesis of low-size FeCu nanoparticles with ideal superparamagnetic properties—exhibiting no hysteresis—suitable for various research and industrial applications.

Transport-of-intensity equation (TIE) as a noninterference method for quantitative phase imaging (QPI) has broad applications in micrographic imaging and optical metrology. Previous TIE-based QPI systems require the axial displacement of the detector to capture the axial intensity distributions, thus limiting the systems’ response speed, integration, and phase retrieval accuracy. Besides, the TIE-based phase imaging for edge positions with large phase gradients remains challenging. In this work, a compact polarization-multiplexed Moiré metalens is proposed to achieve QPI and edge-enhanced imaging for high-precision and unwrapping phase imaging. This Moiré metalens enables continuous zooming from 58.7 μm to 61.8 μm, allowing flexible selection of the detection positions. Under x-polarization light incidence, the metalens can achieve phase retrieval based on the TIE method, with the Root Mean Square Errors (RMSE) reaching 0.015 rad. Under y-polarization light incidence, the metalens realizes varifocal edge-enhanced imaging for amplitude and phase objects, with a minimum spatial resolution of 1.3 μm. This Moiré metalens opens a new avenue to develop compact, integrated, and multifunctional phase imaging devices and has potential applications in optical detection, microscopy, and biomedical imaging.

Conspecuts Commercial and emerging renewable energy technologies are underpinned by precious metal catalysts, which enable the transformation of reactants into useful products. However, the noble metals (NMs) comprise the least abundant elements in the lithosphere, making them prohibitively scarce and expensive for future global-scale technologies. As such, intense research efforts have been devoted to eliminating or substantially reducing the loadings of NMs in various catalytic applications. These efforts have resulted in a plethora of heterogeneous NM catalyst morphologies beyond the traditional supported spherical nanoparticle. In many of these new architectures, such as shaped, high index, and bimetallic particles, less than 20% of the loaded NMs are available to perform catalytic turnovers. The majority of NM atoms are subsurface, providing only a secondary catalytic role through geometric and ligand effects with the active surface NM atoms. A handful of architectures can approach 100% NM utilization, but severe drawbacks limit general applicability. For example, in addition to problems with stability and leaching, single atom and ultrasmall cluster catalysts have extreme metal-support interactions, discretized d-bands, and a lack of adjacent NM surface sites. While monolayer thin films do not possess these features, they exhibit such low surface areas that they are not commercially relevant, serving predominantly as model catalysts. This Account champions core-shell nanoparticles (CS NPs) as a vehicle to design highly active, stable, and low-cost materials with high NM utilization for both thermo- and electrocatalysis. The unique benefits of the many emerging NM architectures could be preserved while their fundamental limitations could be overcome through reformulation via a core-shell morphology. However, the commercial realization of CS NPs remains challenging, requiring concerted advances in theory and manufacturing. We begin by formulating seven constraints governing proper core material design, which naturally point to early transition metal ceramics as suitable core candidates. Two constraints prove extremely challenging. The first relates to the core modifying the shell work function and d-band. To properly investigate materials that could satisfy this constraint, we discuss our development of a new heat, quench, and exfoliation (HQE) density functional theory (DFT) technique to model heterometallic interfaces. This technique is used to predict how transition metal carbides can favorably tune the catalytic properties of various NM monolayer shell configurations. The second challenging constraint relates to the scalable manufacturing of CS NP architectures with independent synthetic control of the thickness and composition of the shell and the size and composition of the core. We discuss our development of a synthetic method that enables high temperature self-assembly of tunable CS NP configurations. Finally, we discuss how these principles and methods were used to design catalysts for a variety of applications. These include the design of a thermally stable sub-monolayer CS catalyst, a highly active methanol electrooxidation catalyst, CO-tolerant Pt catalysts, and a hydrogen evolution catalyst that is less expensive than state-of-the-art NM-free catalysts. Such core-shell architectures offer the promise of ultralow precious metal loadings while ceramic cores hold the promise of thermodynamic stability and access to unique catalytic activity/tunability.

Selective oxidation of methane (SOM) offers a sustainable pathway for energy conversion and chemical synthesis. This review critically compares noble metal (Au, Pd, Ru, Rh) and non-noble metal (Fe, Cu, Cr, Zn, Ni) catalysts for methane activation at low temperatures, evaluating their performance under H<sub>2</sub>O<sub>2</sub> and O<sub>2</sub> as oxidants in environments, with CO as a promoter. Through a detailed analysis of the structure of typical systems, we have established key design principles involving active site engineering, metal-support interactions, and reactive oxygen species. Advanced characterization and density functional theory studies reveal that metal-oxygen interfaces govern methane activation mechanisms, where dynamic oxygen species, such as O*, OH*, and OOH*, dictate reaction pathways. Catalyst dimensionality, such as single-atom vs. clusters, and electronic modifications are shown to critically influence C–H bond cleavage energetics and methanol desorption. While noble metals excel in oxygen activation, modified non-noble catalysts achieve comparable efficacy by optimizing their coordination environments. This review summarizes recent advances in the SOM under mild conditions, providing a systematic qualitative and quantitative kinetic comparison of noble metal and non-noble metal catalysts across various oxidant systems. It offers valuable insights into reaction pathways and mechanisms in different catalytic environments, contributing to a deeper understanding of methane activation and functionalization. It is anticipated that this review will provide a useful guide to chemists and materials scientists attempting to design better metal catalysts for the SOM.

Bimetallic nanostructures with magnetic and noble metals and their physicochemical applications

Squaraine dyes: The hierarchical synthesis and its application in optical detection

Structure illumination microscopy (SIM) is a novel imaging technique with advantages of high spatial resolution, wide imaging field and fast imaging speed. By illuminating the sample with patterned light and analyzing the information about Moir fringes outside the normal range of observation, SIM can achieve about 2-fold higher in resolution than the diffraction limit, thus it has played an important role in the field of biomedical imaging. In recent years, to further improve the resolution of SIM, people have proposed a new technique called plasmonic SIM (PSIM), in which the dynamically tunable sub-wavelength surface plasmon fringes are used as the structured illuminating light and thus the resolution reaches to 3-4 times higher than the diffraction limit. The PSIM technique can also suppress the background noise and improve the signal-to-noise ratio, showing great potential applications in near-surface biomedical imaging. In this review paper, we introduce the principle and research progress of PSIM. In Section 1, we first review the development of optical microscope, including several important near-field and far-field microscopy techniques, and then introduce the history and recent development of SIM and PSIM techniques. In Section 2, we present the basic theory of PSIM, including the dispersion relation and excitation methods of surface plasmon, the principle and imaging process of SIM, and the principle of increasing resolution by PSIM. In Section 3, we review the recent research progress of two types of PSIMs in detail. The first type is the nanostructure-assisted PSIM, in which the periodic metallic nanostructures such as grating or antenna array are used to excite the surface plasmon fringes, and then the shift of fringes is modulated by changing the angle of incident light. The resolution of such a type of PSIM is mainly dependent on the period of nanostructure, thus can be improved to a few tens of nanometers with deep-subwavelength structure period. The other type is the all-optically controlled PSIM, in which the structured light with designed distribution of phase or polarization (e.g. optical vortex) is used as the incident light to excite the surface plasmon fringes on a flat metal film, and then the fringes are dynamically controlled by modulating the phase or polarization of incident light. Without the help of nanostructure, such a type of PSIM usually has a resolution of about 100 nm, but benefits from the structureless excitation of plasmonic fringes in an all-optical configuration, thereby showing more dynamic regulation and reducing the need to fabricate nanometer-sized complex structures. In the final Section, we summarize the features of PSIM and discuss the outlook for this technique. Further studies are needed to improve the performance of PSIM and to expand the scope of practical applications in biomedical imaging.

Near-infrared (NIR) phosphors have been extensively studied in recent years due to their wide-ranging applications in plant growth lighting, night vision, and biomedical imaging. However, poor thermal stability has significantly limited their practical applications in many fields. This paper reports a NIR phosphor with dual emission centers, Li2Ge7O15:Cr3+ (LG:Cr3+). Cr1 located at [GeO6] produces narrow-line emission through the spin-forbidden 2E → 4A2 transition, and as the temperature increases, electrons in the 2E level thermally migrate to the 4T2 level, resulting in emission from the 4T2 state. In contrast, Cr2 located at [LiO6] generates broadband emission through the spin-allowed 4T2 → 4A2 transition, but this emission disappears due to thermal quenching effects as the temperature increases, making it undetectable at room temperature. The optical thermometry applications of the LG:Cr3+ phosphor were investigated using the fluorescence intensity ratio (FIR) technique, achieving a relative sensitivity (Sr) of 2.77% K-1 at 100 K. Finally, a NIR phosphor-converted diode (NIR pc-LED) was fabricated using this phosphor and a 410 nm chip, demonstrating potential applications in biomedical imaging and night vision.

Nobel metal nanomaterials with interesting physical and chemical properties are ideal building blocks for engineering and tailoring nanoscale structures for specific technological applications. Bimetallic nanomaterials consisting of magnetic metals and noble metals have attracted much interest for their promising potentials in many fields including magnetic sensors, catalysts, optical detection, and biomedical applications. Particularly, effective control of the size, shape, architecture, and compositional microstructure of metal nanomaterials plays an important role in enhancing their functionality and application potentials, for example, in fuel cells, optical and biomedical sensing. This paper focuses on recent advances in controllable synthesis of bimetallic nanostructured materials. Recent contributions in controllable synthesis of bimetallic nanomaterials with different architectures including nanoparticles, nanowires, nanosheets, or nanotubes and their assemblies are presented in this paper. A wide range of facile synthesis methods are covered herein with high emphasis on wet chemical methods owing to their facility of use, efficacy, and smaller environmental footprint.

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a priori, knowledge about the type of noise corrupting the image. This makes the standard filters application specific. Widely used filters such as average, Gaussian, and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high-frequency details, making the image nonsmooth. An integrated general approach to design a finite impulse response filter based on Hebbian learning is proposed for optimal image filtering. This algorithm exploits the interpixel correlation by updating the filter coefficients using Hebbian learning. The algorithm is made iterative for achieving efficient learning from the neighborhood pixels. This algorithm performs optimal smoothing of the noisy image by preserving high-frequency as well as low-frequency features. Evaluation results show that the proposed finite impulse response filter is robust under various noise distributions such as Gaussian noise, salt-and-pepper noise, and speckle noise. Furthermore, the proposed approach does not require any a priori knowledge about the type of noise. The number of unknown parameters is few, and most of these parameters are adaptively obtained from the processed image. The proposed filter is successfully applied for image reconstruction in a positron emission tomography imaging modality. The images reconstructed by the proposed algorithm are found to be superior in quality compared with those reconstructed by existing PET image reconstruction methodologies.

Structured illumination microscopy (SIM) has been widely applied to investigating fine structures of biological samples by breaking the optical diffraction limitation. So far, video-rate imaging has been obtained in SIM, but the imaging speed was still limited due to the reconstruction of a super-solution image through multi-sampling, which hindered the applications in high-speed biomedical imaging. To overcome this limitation, here we develop compressive imaging-based structured illumination microscopy (CISIM) by synergizing SIM and compressive sensing (CS). Compared with conventional SIM, CISIM can greatly improve the super-resolution imaging speed by extracting multiple super-resolution images from one compressed image. Based on CISIM, we successfully reconstruct the super-resolution images in biological dynamics, and analyze the effect factors of image reconstruction quality, which verify the feasibility of CISIM. CISIM paves a way for high-speed super-resolution imaging, which may bring technological breakthroughs and significant applications in biomedical imaging.

Electrodeposition of Ni32Fe48Mo20 and Ni52Fe33W15 alloy film on Cu microwire from ionic liquid containing plating bath

The quest for sustainable and high-efficiency energy conversion technologies has driven intense research into oxygen reduction reaction (ORR) catalysts, particularly those based on noble metals, due to the harsh redox environment of these devices. Despite their unparalleled activity, the scarcity and high cost of noble metals like platinum remain significant bottlenecks for large-scale application. Recent advances in surface and interface engineering of noble metal heterostructures offer a promising pathway to address these challenges. By precisely tailoring surface atomic arrangements, modulating interfacial electronic structures, and constructing synergistic heterojunctions, researchers have unlocked unprecedented catalytic efficiencies with dramatically reduced metal loading. This review systematically explores the latest strategies in designing surface- and interface-optimized noble metal heterostructures for ORR, highlighting how these innovations enable maximum utilization of active sites while enhancing activity and durability. We delve into various synthesis techniques, structural modulation approaches, and mechanistic insights gained from advanced characterization and theoretical modeling. Special emphasis is placed on understanding the critical role of heterointerfaces in tuning adsorption energies, charge transfer dynamics, and reaction pathways. Finally, we outline current challenges and propose future directions for the rational design of next-generation ORR catalysts that combine minimal noble metal usage with exceptional performance.

Mathematics in Biomedical Imaging

Image filtering techniques have numerous potential applications in biomedical imaging and image processing. The design of filters largely depends on the a-priori knowledge about the type of noise corrupting the image and image features. This makes the standard filters to be application and image specific. The most popular filters such as average, Gaussian and Wiener reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design filters based on discrete cosine transform (DCT) is proposed in this study for optimal medical image filtering. This algorithm exploits the better energy compaction property of DCT and re-arrange these coefficients in a wavelet manner to get the better energy clustering at desired spatial locations. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions.