Search Results (21)

Mn⁴⁺-doped fluoride red phosphors are widely used in white LED lighting and display applications due to their excellent luminescent properties. However, their synthesis relies heavily on highly toxic aqueous hydrofluoric acid, which not only causes severe environmental and soil/water pollution but also makes it difficult to control the microstructure of the products due to the rapid reaction rate. In this study, low-melting-point NH₄HF₂ was used as the molten salt, with KMnO₄ and MnF₂ as manganese sources, to synthesize the red phosphor K₃AlF₆: Mn⁴⁺ via the molten salt method. After the reaction, impurities such as NH₄HF₂ were removed by washing with a dilute H₂O₂ solution. The microstructure, photoluminescence properties, thermal quenching behavior, and application in warm white light-emitting diodes (W-LEDs) of the K₃AlF₆: Mn⁴⁺ phosphors were investigated. The results indicate that the phosphors prepared by this method consist of a single pure phase. By adjusting the molten salt content, the morphology of the product can be transformed from nanoparticle-like to nanorod-like structures. All products exhibit the characteristic red emission of Mn⁴⁺ under blue and violet light excitation, with the optimally doped sample achieving an internal quantum efficiency (IQE) of 69% under blue light excitation. The combination of the obtained K₃AlF₆: Mn⁴⁺ with the yellow phosphor YAG enabled the fabrication of W-LEDs. These W-LEDs achieved a color rendering index (Ra) of 86.8, a luminous efficacy (LE) of 77 lm/W, and a correlated color temperature (CCT) of 3690 K, along with excellent color stability under operating conditions. Full article

In this study, a green hydrothermal synthesis method was employed to produce Mn₃O₄ and Mn₃O₄/β-MnO₂ nanostructures using EET-50, an organic extract obtained from a by-product of Carménère wine production. The biomolecules in EET-50 acted as reducing agents due to their electron-donating functional groups, enabling nanostructure formation without the need for additional chemical reductants. Morphological characterization by SEM revealed that a KMnO₄/EET-50 mass ratio of 3:1 led to the synthesis of nano-octahedra alongside rod-like structures, with shorter reaction times favoring the development of isolated nano-octahedra ranging from 100 nm to 170 nm. Structural analyses by XRD and Raman spectroscopy confirmed the formation of mixed-phase Mn₃O₄/β-MnO₂ and Mn₃O₄ (hausmannite). Electrochemical performance tests demonstrated that Mn₃O₄ nano-octahedra exhibited a superior specific capacitance of 236.27 F/g at 1 mA/g, surpassing the mixed-phase sample by 28.3%, and showed excellent capacitance retention (99.98%) after 100 cycles at 8 mA/g. Additionally, the Mn₃O₄ nano-octahedra exhibited enhanced oxygen evolution reaction performance in alkaline media, with an overpotential of 0.430 V vs. RHE and a Tafel slope of 205 mV/dec. These results underscore the potential of Mn₃O₄ nano-octahedra, synthesized via a green route using grape pomace extract as a reducing agent, offering an environmentally friendly alternative for applications in energy storage and electrocatalysis. Full article

Hierarchical porous carbon derived from discarded biomass for energy storage materials has attracted increasing research attention due to its cost-effectiveness, ease of fabrication, environmental protection, and sustainability. Brewed tea leaves are rich in heteroatoms that are beneficial to capacitive energy storage behavior. Therefore, we synthesized high electrochemical performance carbon-based composites from Tie guan yin tea leaf waste using a facile procedure comprising hydrothermal, chemical activation, and calcination processes. In particular, potassium permanganate (KMnO₄) was incorporated into the potassium hydroxide (KOH) activation agent; therefore, during the activation process, KOH continued to erode the biomass precursor, producing abundant pores, and KMnO₄ synchronously underwent a redox reaction to form MnO nanoparticles and anchor on the porous carbon through chemical bonding. MnO nanoparticles provided additional pseudocapacitive charge storage capabilities through redox reactions. The results show that the amount of MnO produced is proportional to the amount of KMnO₄ incorporated. However, the specific surface area of the composite material decreases with the incorporated amount of KMnO₄ due to the accumulation and aggregation of MnO nanoparticles, thereby even blocking some micropores. Optimization of MnO nanocrystal loading can promote the crystallinity and graphitization degree of carbonaceous materials. The specimen prepared with a weight ratio of KMnO₄ to hydrochar of 0.02 exhibited a high capacitance of 337 F/g, an increase of 70%, owing to the synergistic effect between the Tie guan yin tea leaf-derived activated carbon and MnO nanoparticles. With this facile preparation method and the resulting high electrochemical performance, the development of manganese oxide/carbon composites derived from tea leaf biomass is expected to become a promising candidate as an energy storage material for supercapacitors. Full article

The airborne hyperspectral remote sensing systems (AHRSSs) acquire images with high spectral resolution, high spatial resolution, and high temporal dimension. While the AHRSS captures more detailed information from the terrain objects, the computational complexity of data processing is greatly increased. As an important application technology in the hyperspectral domain, anomaly detection (AD) processing must be real-time and high-precision in many cases, such as post-disaster rescue, military battlefield search, and natural disaster detection. In this paper, the real-time AD technology for the push-broom AHRSS is studied, the mathematical model is established, and a novel implementation framework is proposed. Firstly, the optimized kernel minimum noise fraction (OP-KMNF) transformation is employed to extract informative and discriminative features between the background and anomalies. Secondly, the Nyström method is introduced to reduce the computational complexity of OP-KMNF transformation by decomposing and extrapolating the sub-kernel matrix to estimate the eigenvector of the entire kernel matrix. Thirdly, the extracted features are transferred to hard disks for data storage. Then, taking the extracted features as input data, the background separation model-based CEM anomaly detector (BSM-CEMAD) is imported to detect anomalies. Finally, graphics processing unit (GPU) parallel computing is utilized in the Nyström-based OP-KMNF (NOP-KMNF) transformation and the BSM-CEMAD to improve the execution efficiency, and the real-time AD for the push-broom AHRSS could be realized. To test the feasibility of the implementation framework proposed in this paper, the experiment is carried out with the Airborne Multi-Modular Imaging Spectrometer (AMMIS) developed by the Shanghai Institute of Technical Physics as the data acquisition platform. The experimental results show that the proposed method outperforms many other state-of-the-art AD methods in anomalies detection and background suppression. Moreover, under the condition that the downlink data could retain most of the hyperspectral data information, the proposed method achieves real-time detection of pixel-level anomalies, with the initial delay not exceeding 1 s, the false alarm rate (FAR) less than 5%, and the true positive rate (TPR) close to 98%. Full article

In this paper, a novel procedure is proposed for comprehensively recovering zinc fluoride (ZnF₂), manganese fluorides [MnF_{x(x = 2, 3)}], manganese dioxide (MnO₂), and carbon powder from the electrode powder of spent alkaline batteries. Firstly, hydrofluoric acid (HF) leaching is conducted on the electrode powder of spent alkaline batteries. Secondly, potassium permanganate (KMnO₄) is introduced into the leachate to selectively precipitate manganese (Mn) ions to recover MnO₂. Subsequently, the water content in the leachate is evaporated to recover ZnF₂. Finally, the leaching residue is leached again by using HF, after which the water content in the leachate is evaporated to recover MnF_x. The results indicated that under optimal conditions of a HF concentration of 4 M, a leaching time of 15 min, and a liquid–solid ratio of 5 mL/g, the optimal leaching selectivity for Zn and Mn was achieved and the leaching efficiencies of Zn and Mn were 97.83% and 39.94%, respectively. When KMnO₄ with a dosage (KMnO₄/Mn ion molar ratio) of 0.5:1 was added to the leachate, MnO₂ with a grade of 91.68% and a Mn recovery of 39.07% was obtained. In addition, ZnF₂ with a grade of 97.98% and a Zn recovery of 96.15% was also obtained after removing the water content from the leachate via evaporation. Under the optimal conditions of a HF concentration of 2 M, a leaching time of 15 min, and a liquid–solid ratio of 10 mL/g for the leaching residue, followed by removing the water content in the leachate via evaporation, MnF_x with a grade of 94.20% and a Mn recovery of 59.46%, was obtained. The residue of the releaching process was carbon powder. The effectiveness of the proposed recovery procedure was confirmed. Full article

Polymer-based waveguide amplifiers are essential components in integrated optical systems, as their gain bandwidths directly determine the operating wavelength of optical circuits. However, development of the wideband gain media has been challenging, making it difficult to fabricate devices with broadband amplification capability. Rare earth ion-doped nanoparticles (NPs) are a key component in the gain media, and their full width at half maximum (FWHM) of the emission peak decides the final gain bandwidth of the gain media. Here, KMnF₃: Yb, Er, Ce@KMnF₃: Yb NPs with the broad full width at half maximum (FWHM) of the emission peak covering the S+C band was prepared. The NPs were synthesized using a hydrothermal method, and the FWHM of the emission peak of NPs reached 76 nm under the excitation of a 980 nm laser. The introduction of Ce³⁺ ions and a core-shell structure coating greatly enhanced the emission intensity of NPs at C band. Since KMnF₃: Yb, Er, Ce@KMnF₃: Yb NPs have exceptional broadband luminescence properties at C band, KMnF₃: Yb, Er, Ce@KMnF₃: Yb NPs can be the potential gain medium in the future polymer-based waveguide amplifiers. Full article

A series of three-dimensional porous composite α-MnO₂/reduced graphene oxides (α-MnO₂/RGO) were prepared by nano-assembly in a hydrothermal environment at pH 9.0–13.0 using graphene oxide as the precursor, KMnO₄ and MnCl₂ as the manganese sources and F⁻ as the control agent of the α-MnO₂ crystal form. The α-MnO₂/RGO composites prepared at different hydrothermal pH levels presented porous network structures but there were significant differences in these structures. The special pore structure promoted the migration of ions in the electrolyte in the electrode material, and the larger specific surface area promoted the contact between the electrode material and the electrolyte ions. The introduction of graphene solved the problem of poor conductivity of MnO₂, facilitated the rapid transfer of electrons, and significantly improved the electrochemical performance of materials. When the pH was 12.0, the specific surface area of the 3D porous composite material αMGs-12.0 was 264 m²·g⁻¹, and it displayed the best super-capacitive performance; in Na₂SO₄ solution with 1.0 mol·L⁻¹ electrolyte, the specific capacitance was 504 F·g⁻¹ when the current density was 0.5 A·g⁻¹ and the specific capacitance retention rate after 5000 cycles was 88.27%, showing that the composite had excellent electrochemical performance. Full article

A significant challenge in methods for anomaly detection (AD) in hyperspectral images (HSIs) is determining how to construct an efficient representation for anomalies and background information. Considering the high-order structures of HSIs and the estimation of anomalies and background information in AD, this article proposes a kernel minimum noise fraction transformation-based background separation model (KMNF-BSM) to separate the anomalies and background information. First, spectral-domain KMNF transformation is performed on the original hyperspectral data to fully mine the high-order correlation between spectral bands. Then, a BSM that combines the outlier removal, the iteration strategy, and the Reed–Xiaoli detector (RXD) is proposed to obtain accurate anomalous and background pixel sets based on the extracted features. Finally, the anomalous and background pixel sets are used as input for anomaly detectors to improve the background suppression and anomaly detection capabilities. Experiments on several HSIs with different spatial and spectral resolutions over different scenes are performed. The results demonstrate that the KMNF-BSM-based algorithms have better target detectability and background suppressibility than other state-of-the-art algorithms. Full article

In this work, MnO₂/TiO₂ coating on metallic porous titanium was prepared through a hydrothermal-based chemical method, followed by a chemical precipitation reaction of KMnO₄ and MnSO₄ aqueous solutions. The surface of the MnO₂/TiO₂/Ti was uniform and compact, with a high load capacity. The corrosion resistance and electrochemical properties of the MnO₂/TiO₂/Ti coating were investigated in comparison with those of pure Ti and TiO₂ coatings. Cyclic voltammetry and constant current charge–discharge measurements showed that the MnO₂/TiO₂/Ti electrode presented good electrochemical performance. The MnO₂/TiO₂/Ti electrode had the highest capacitor performance compared to the other electrodes, and the nano-MnO₂ coating significantly decreased the corrosion current densities. The nano-MnO₂ coating exhibited excellent anti-corrosion properties at room temperature and better capacitance performance compared with pure Ti and TiO₂ coatings. After surface modification, TiO₂/Ti-coated MnO₂ had better electrochemical behavior and significantly improved corrosion resistance than the TiO₂/Ti nanocomposites. Its specific capacitance reached 314 F/g, which was 3.5 times that of the TiO₂/Ti electrode material. Full article

Recently, significant progress has been made in the performance of supercapacitors through the development of composite electrodes that combine various charge storage mechanisms. A new method for preparing composite binder-free MnO₂/MWCNT/Al electrodes for supercapacitors is proposed. The method is based on the original technique of direct growth of layers of multi-walled carbon nanotubes (MWCNTs) on aluminum foil by the catalytic pyrolysis of ethanol vapor. Binder-free MnO₂/MWCNT/Al electrodes for electrochemical supercapacitors were obtained by simply treating MWCNT/Al samples with an aqueous solution of KMnO₄ under mild conditions. The optimal conditions for the preparation of MnO₂/MWCNT/Al electrodes were found. The treatment of MWCNT/Al samples in a 1% KMnO₄ aqueous solution for 40 min increased the specific capacitance of the active material of the samples by a factor of 3, up to 100–120 F/g. At the same time, excellent adhesion and electrical contact of the working material to the aluminum substrate were maintained. The properties of the MnO₂/MWCNT/Al samples were studied by electron probe microanalysis (EPMA), Raman spectroscopy, cyclic voltammetry (CV), and impedance spectroscopy. Excellent charge/discharge characteristics of composite electrodes were demonstrated. The obtained MnO₂/MWCNT/Al electrodes maintained excellent stability to multiple charge-discharge cycles. After 60,000 CVs, the capacitance loss was less than 20%. Thus, this work opens up new possibilities for using the MWCNT/Al material obtained by direct deposition of carbon nanotubes on aluminum foil for the fabrication of composite binder-free electrodes of supercapacitors. Full article

Feature extraction, aiming to simplify and optimize data features, is a typical hyperspectral image dimensionality reduction technique. As a kernel-based method, kernel minimum noise fraction (KMNF) transformation is excellent at handling the nonlinear features within HSIs. It adopts the kernel function to ensure data linear separability by transforming the original data to a higher feature space, following which a linear analysis can be performed in this space. However, KMNF transformation has the problem of high computational complexity and low execution efficiency. It is not suitable for the processing of large-scale datasets. In terms of this problem, this paper proposes a graphics processing unit (GPU) and Nyström method-based algorithm for Fast KMNF transformation (GNKMNF). First, the Nyström method estimates the eigenvector of the entire kernel matrix in KMNF transformation by the decomposition and extrapolation of the sub-kernel matrix to reduce the computational complexity. Then, the sample size in the Nyström method is determined utilizing a proportional gradient selection strategy. Finally, GPU parallel computing is employed to further improve the execution efficiency. Experimental results show that compared with KMNF transformation, improvements of up to 1.94% and 2.04% are achieved by GNKMNF in overall classification accuracy and Kappa, respectively. Moreover, with a data size of 64 × 64 × 250, the execution efficiency of GNKMNF speeds up by about 80×. The outcome demonstrates the significant performance of GNKMNF in feature extraction and execution efficiency. Full article

Morphologically tunable copper oxide-based nanomaterials on Cu wire have been synthesized through a one-step alkali-assisted surface oxidation process for non-enzymatic glucose sensing. Subsequently, copper oxide-based nanomaterials on Cu wire as a supporting matrix to deposit manganese oxide for the construction of heterostructured Mn-Cu bimetallic oxide architectures through spontaneous redox reaction in the KMnO₄ solution for supercapacitors. Field emission scanning electron microscopy (FESEM), field emission transmission electron microscopy (FETEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) confirmed that morphological and phase transformation from Cu(OH)₂ to CuO occurred in copper oxide-based nanomaterials on Cu wire with different degrees of growth reaction. In non-enzymatic glucose sensing, morphologically tunable copper oxide-based nanomaterials owned the high tunability of electrocatalytically active sites and intrinsic catalytic activity to meet efficient glucose electrooxidation for obtaining promoted non-enzymatic glucose sensing performances (sensitivity of 2331 μA mM⁻¹ cm⁻² and the limit of detection of 0.02 mM). In the supercapacitor, heterostructured Mn–Cu bimetallic oxide-based nanomaterials delivered abundant redox-active sites and continuous conductive network to optimize the synergistic effect of Mn and Cu redox species for boosting the pseudo-capacitance performance (areal capacitance value of 79.4 mF cm⁻² at 0.2 mA cm⁻² current density and capacitance retention of 74.9% after 1000 cycles). It concluded that morphologically tunable copper oxide-based nanomaterials on Cu wire with/without deposition of manganese oxide could be good candidates for the future design of synergistic multifunctional materials in electrochemical techniques. Full article

In this study, carbon-supported MnO₂ nanocomposites have been prepared using the microwave-assisted heating method followed by two different approaches. The MnO₂/C nanocomposite, labeled as sample S1, was prepared directly by the microwave-assisted synthesis of mixed KMnO₄ and carbon powder components. Meanwhile, the other MnO₂/C nanocomposite sample labeled as S2 was prepared indirectly via a two-step procedure that involves the microwave-assisted synthesis of mixed KMnO₄ and MnSO₄ components to generate MnO₂ and subsequent secondary microwave heating of synthesized MnO₂ species coupled with graphite powder. Field emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and inductively coupled plasma optical emission spectroscopy have been used for characterization of MnO₂/C nanocomposites morphology, structure, and composition. The electrochemical performance of nanocomposites has been investigated using cyclic voltammetry and galvanostatic charge/discharge measurements in a 1 M Na₂SO₄ solution. The MnO₂/C nanocomposite, prepared indirectly via a two-step procedure, displays substantially enhanced electrochemical characteristics. The high specific capacitance of 980.7 F g⁻¹ has been achieved from cyclic voltammetry measurements, whereas specific capacitance of 949.3 F g⁻¹ at 1 A g⁻¹ has been obtained from galvanostatic charge/discharge test for sample S2. In addition, the specific capacitance retention was 93% after 100 cycles at 20 A g⁻¹, indicating good electrochemical stability. Full article

Dimensionality reduction (DR) is of great significance for simplifying and optimizing hyperspectral image (HSI) features. As a widely used DR method, kernel minimum noise fraction (KMNF) transformation preserves the high-order structures of the original data perfectly. However, the conventional KMNF noise estimation (KMNF-NE) uses the local regression residual of neighbourhood pixels, which depends heavily on spatial information. Due to the limited spatial resolution, there are many mixed pixels in HSI, making KMNF-NE unreliable for noise estimation and leading to poor performance in KMNF for classification on HSIs with low spatial resolution. In order to overcome this problem, a mixed noise estimation model (MNEM) is proposed in this paper for optimized KMNF (OP-KMNF). The MNEM adopts the sequential and linear combination of the Gaussian prior denoising model, median filter, and Sobel operator to estimate noise. It retains more details and edge features, making it more suitable for noise estimation in KMNF. Experiments using several HSI datasets with different spatial and spectral resolutions are conducted. The results show that, compared with some other DR methods, the improvement of OP-KMNF in average classification accuracy is up to 4%. To improve the efficiency, the OP-KMNF was implemented on graphics processing units (GPU) and sped up by about 60× compared to the central processing unit (CPU) implementation. The outcome demonstrates the significant performance of OP-KMNF in terms of classification ability and execution efficiency. Full article

Fibers made from CNTs (CNT fibers) have the potential to form high-strength, lightweight materials with superior electrical conductivity. CNT fibers have attracted great attention in relation to various applications, in particular as conductive electrodes in energy applications, such as capacitors, lithium-ion batteries, and solar cells. Among these, wire-shaped supercapacitors demonstrate various advantages for use in lightweight and wearable electronics. However, making electrodes with uniform structures and desirable electrochemical performances still remains a challenge. In this study, dry-spun CNT fibers from CNT carpets were homogeneously loaded with MnO₂ nanoflakes through the treatment of KMnO₄. These functionalized fibers were systematically characterized in terms of their morphology, surface and mechanical properties, and electrochemical performance. The resulting MnO₂–CNT fiber electrode showed high specific capacitance (231.3 F/g) in a Na₂SO₄ electrolyte, 23 times higher than the specific capacitance of the bare CNT fibers. The symmetric wire-shaped supercapacitor composed of CNT–MnO₂ fiber electrodes and a PVA/H₃PO₄ electrolyte possesses an energy density of 86 nWh/cm and good cycling performance. Combined with its light weight and high flexibility, this CNT-based wire-shaped supercapacitor shows promise for applications in flexible and wearable energy storage devices. Full article