Optimal Band Research Articles

A Leaf Chlorophyll Content Estimation Method for Populus deltoides (Populus deltoides Marshall) Using Ensembled Feature Selection Framework and Unmanned Aerial Vehicle Hyperspectral Data

Leaf chlorophyll content (LCC) is a key indicator in representing the photosynthetic capacity of Populus deltoides (Populus deltoides Marshall). Unmanned aerial vehicle (UAV) hyperspectral imagery provides an effective approach for LCC estimation, but the issue of band redundancy significantly impacts model accuracy and computational efficiency. Commonly used single feature selection algorithms not only fail to balance computational efficiency with optimal set search but also struggle to combine different regression algorithms under dynamic set conditions. This study proposes an ensemble feature selection framework to enhance LCC estimation accuracy using UAV hyperspectral data. Firstly, the embedded algorithm was improved by introducing the SHapley Additive exPlanations (SHAP) algorithm into the ranking system. A dynamic ranking strategy was then employed to remove bands in steps of 10, with LCC models developed at each step to identify the initial band subset based on estimation accuracy. Finally, the wrapper algorithm was applied using the initial band subset to search for the optimal band subset and develop the corresponding model. Three regression algorithms including gradient boosting regression trees (GBRT), support vector regression (SVR), and gaussian process regression (GPR) were combined with this framework for LCC estimation. The results indicated that the GBRT-Optimal model developed using 28 bands achieved the best performance with R2 of 0.848, RMSE of 1.454 μg/cm2 and MAE of 1.121 μg/cm2. Compared with a model performance that used all bands as inputs, this optimal model reduced the RMSE value by 24.37%. In addition to estimating biophysical and biochemical parameters, this method is also applicable to other hyperspectral imaging tasks.

Nanocomposite Material Sb2S3:SnS:MnS2 its Fabrication and Utilization as Efficient Electrode for Energy Storage in Supercapacitor

AbstractThe nanocomposite material Sb2S3: SnS:MnS2 was fabricated by single precursor technique by using dithio‐carbamate ligand as Sb2S3:SnS:MnS2 (DDTC) complex. An internal and external look at the anatomy and activity of the substance was obtained by using a variety of analytical techniques like XRD, FTIR, UV‐Vis and SEM. Additionally, various electro‐analytical tools like LSV, CV, GCD and EIS were applied to confirm material's rich electro‐active capability for photoactive electrode. XRD revealed its average crystallite size of 12.33 nm with 82.8 % crystallinity. Optical characteristics represented describe effective semiconducting nature of nanomaterials with optimum band gap of 2.65 eV. By using electrochemical processes, LSV and CV demonstrated fertile opto‐electrical nature of Sb2S3: SnS: MnS2 (DDTC) material with specific capacitance, Csp, of 1080 F/g at scan rate of 2 mV/s. With a high specific power density of 2656 W/kg, the GCD technique attempted to validate the nature of the material as an electrode with a good charge‐discharge mechanism. In EIS, low series resistance, Rs=0.73 Ω, was observed to be very beneficial argument in validating the nanocomposite synthesized in this research project as effective and low‐cost material which can be applied in different energy producing and storing devices like solar system and super‐capacitors etc.

Computational screening of 2D ternary penta-materials with auxetic properties and efficient photocatalytic CO2 reduction

Due to unique structural and electronic properties, pentagon-based two-dimensional (2D) materials have captured significant interest in recent years. However, previous studies on these materials have primarily focused on unary and binary systems, with less exploration of ternary pentagonal structures. Herein, we conducted first-principles calculations to evaluate the stability of 122 ternary pentagonal monolayers composed of metals, nitrogen, and chalcogen. We identify six highly stable candidates, including AuNS, CuNTe, GaNS, InNS, SbNS, and SbNSe that meet the criteria for thermodynamic, mechanical, and dynamic stability. Mechanical property analysis reveals that five of these monolayers demonstrate auxetic behavior. Electronic structure calculations show that all candidates are semiconductors with band gaps ranging from 0.12 to 3.42 eV. Specifically, the SbNS and SbNSe monolayers are indirect semiconductors with band gaps of 2.25 and 2.27 eV, respectively. These materials exhibit strong visible light absorption, and potentially serve as exceptional photocatalysts for the reduction of CO2 to CH4 due to the optimal band alignment. The free energy change in the rate-limiting step is even lower than those found in g-C3N4 and MoS2-based systems. Our findings highlight new pentagon-based 2D materials with remarkable electronic, mechanical, and optical properties, making them promising candidates for applications in mechanics and energy conversion.

A comparative study of BiFeO3-based compounds for enhanced hydrogen evolution reaction

We present a comprehensive study, combining experimental and theoretical approaches, to assess the hydrogen evolution reaction (HER) efficiency of BiFeO3-based solid solutions. Initially, we investigate the electronic and optical properties of these compounds, with a particular focus on band edge alignment relative to water redox potentials. Our findings show that the materials exhibit optimal band gaps of approximately 2.0 eV, indicative of enhanced visible light absorption and favorable energetic alignment to drive efficient hydrogen generation. To corroborate our theoretical predictions, we perform photoelectrochemical measurements on selected BiFeO3-based compounds synthesized via the solid-state method. Our experimental results reveal a high hydrogen yield, with BiFeO3-SrTiO3 achieving a production rate of ∼114 µmol/L in 30 min, outperforming BiFeO3-BaTiO3 (∼70 µmol/L) and pristine BiFeO3 (∼61 µmol/L). These findings validate our theoretical assumptions and demonstrate the superior HER performance of BiFeO3-SrTiO3, positioning it as a highly promising candidate for sustainable hydrogen production.

A novel recursive sub-tensor hyperspectral compressive sensing of plant leaves based on multiple arbitrary-shape regions of interest

Plant hyperspectral images (HSIs) contain valuable information for agricultural disaster prediction, biomass estimation, and other applications. However, they also include a lot of irrelevant background information, which wastes storage resources. In this paper, we propose a novel recursive sub-tensor hyperspectral compressive sensing method for plant leaves. This method uses recursive sub-tensor compressive sensing to compress and reconstruct each arbitrary-shape leaf region, discarding a large amount of background information to achieve the best possible reconstruction performance of the leaf region and significantly reduce storage space. The proposed method involves several key steps. Firstly, the optimal band is determined using the spatial spectral decorrelation criterion, and its corresponding mask image is used to extract the leaf regions from the background. Secondly, the recursive maximum inscribed rectangle algorithm is applied to obtain rectangular sub-tensors of leaves recursively. Each sub-tensor is then individually compressed and reconstructed. Finally, all sub-tensors can be reconstructed to form complete leaf HSIs without background information. Experimental results demonstrate that the proposed method achieves superior image reconstruction quality at extremely low sampling rates compared to other methods. The proposed method can improve average Peak Signal-to-Noise Ratio (PSNR) values by about 3.04% and 0.74% compared to Tensor Compressive Sensing (TCS) at the sampling rate of 2%. In the spectral domain, the proposed method can achieve significantly smaller Spectral Angle Mapper (SAM) values and relatively lower spectral indices errors for Double Difference, Triangular Vegetation Index, Leaf Chlorophyll Index, and Modified Normalized Difference 680 than those of TCS. Therefore, the proposed method achieves better compression performance for reconstructed plant leaf HSIs than the other methods.

Mapping Leaf Mass Per Area and Equivalent Water Thickness from PRISMA and EnMAP

With the continued advancement of spaceborne hyperspectral sensors, hyperspectral remote sensing is evolving as an increasingly pivotal tool for high-precision global monitoring applications. Novel image spectroscopy data, e.g., the PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Environmental Mapping and Analysis Program (EnMAP), can rapidly and non-invasively capture subtle spectral information of terrestrial vegetation, facilitating the precise retrieval of the required vegetation parameters. As critical vegetation traits, Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) hold significant importance for comprehending ecosystem functionality and the physiological status of plants. To address the demand for high-precision vegetation parameter datasets, a hybrid modeling approach was proposed in this study, integrating the radiative transfer model PROSAIL and neural network models to retrieve LMA and EWT from PRISMA and EnMAP images. To achieve this objective, canopy reflectance was simulated via PROSAIL, and the optimal band combinations for LMA and EWT were selected as inputs to train neural networks. The evaluation of the hybrid inversion models over field measurements showed that the RMSE values for the LMA and EWT were 4.11 mg·cm−2 and 9.08 mg·cm−2, respectively. The hybrid models were applied to PRISMA and EnMAP images, resulting in LMA and EWT maps displaying adequate spatial consistency, along with cross-validation results showing high accuracy (RMSELMA = 5.78 mg·cm−2, RMSEEWT = 6.84 mg·cm−2). The results demonstrated the hybrid inversion model’s universality and applicability, enabling the retrieval of vegetation parameters from image spectroscopy data and offering a valuable contribution to hyperspectral remote sensing for vegetation monitoring, though the availability of field measurement data remained a significant challenge.

Estimation of the soluble solid content of citrus based on the fractional-order derivative and optimal band combination algorithm.

The soluble solid content (SSC) is a primary characteristic index for evaluating the internal quality of citrus fruits. The development of rapid and nondestructive SSC detection techniques can help address the current issues of postharvest quality grading in China's citrus industry. In this study, a total of 261 experimental samples, including 70 Murcott, 91 Clementine, and 100 Navel orange, were divided into prediction and validation sets in a 7:3 ratio. After obtaining the reflection spectra and SSCs, SNV-FOD (Standard Normal Variate-Fractional-Order Derivative) was used to process the spectra, and the optimal band combination algorithm was introduced to select SSC-sensitive bands. Then, the obtained optimal dual-band combination was input into eight regression models for comparison, and the best performing models stacked ensemble models was selected. Finally, the H-ELR (HyperOpt-optimized ensemble learning regression) model, optimized using a Bayesian function, was applied for the effective estimation of SSC for three common citrus varieties in Guangxi, Murcott, Clementine, and Navel oranges. The results show that (1) the SNV-FOD preprocessing method proposed in this study improved the correlation coefficient with the SSC from 0.546 to 0.836 compared to that of the original spectrum, (2) the optimal dual-band combination (969 and 1069nm) constructed by integrating the differential index and 1.2-order derivative yielded the most accurate results (RPD=2.13), and (3) the H-ELR model, based on HyperOpt optimization, achieved good estimated performance (RPD=2.46). PRACTICAL APPLICATION: This research contributes to the development of practical SSC prediction instruments with excellent universality and ease of application.

An inorganic lead-free Cs2SnI6-based perovskite solar cell optimization by SCAPS-1D

Cs2SnI6 is an environmentally friendly and reliable perovskite solar cell (PSCs) material. Its optimal band gap and strong light absorption make it a promising candidate for the absorption layer. However, the current challenge is to improve its photoelectric conversion efficiency. To address this, this study investigates the performance of PSCs based on Cs2SnI6 using SCAPS-1D simulation. The influence of various factors on PSC performance is examined, including different hole transport layers(HTLs) and electron transport layers(ETLs), perovskite layer thickness, ETL doping density, HTL doping density, absorber doping density, perovskite layer defect density, different back contacts and temperature. Finally, a Cs2SnI6-based solar device with an inorganic configuration of FTO/NiO/Cs2SnI6/SnO2/Au has been developed, reaching the power conversion efficiency(PCE) of 28.69 %. The study demonstrates that Cs2SnI6 PSCs exhibit promising photovoltaic performance, offering valuable insights for the solar energy sector in the production of cost-effective, efficient, and environmentally friendly Cs-based perovskite solar cells.

Investigation of annealing temperature effect on structural, morphological, optical, magnetic, and dielectric properties of cobalt-doped manganese ferrites

Spinel ferrite Mn0.95Co0.05Fe2O4 was prepared via chemical co-precipitation routes and annealed at four distinct temperatures of 600 °C, 650 °C, 700 °C, and 750 °C. The XRD peak confirmed a single-phase cubic spinel structure, with an increasing crystallite size of 21.64–23.98 nm by the annealing effect. The TEM images show an increase in particle size with rising annealing temperature and indicate well-defined nanocrystalline Mn-Co ferrite. FT-IR spectra detected two vibrational modes at interstitial sub-lattice sites, identifying Co-O, Mn-O, and Fe-O bonds with the spinel structure of Mn-Co ferrite. UV–vis–NIR reflectance revealed an optimal direct band gap of 3.76–4.70 eV, indicating a scope of semiconducting behaviors in the nanocatalysts. In the magnetic hysteresis curve, saturation magnetization decreased while coercivity increased with rising annealing and crystallite size. The M-T loop reveals the Curie temperature of Mn-Co ferrite decreases from 533.84 °C to 382.74 °C. Frequency-dependent complex permeability at different annealing temperatures shows stable initial permeability over a wide frequency range (1 kHz to 100 MHz) and a quality factor several times higher due to reduced magnetic tangent loss. The real dielectric constant and losses decline in rising alternating fields (100 Hz to 1 MHz) and stabilize at higher frequencies following Maxwell-Wagner-type polarization. Eventually, structural, morphological, and magnetic properties are performed relatively well at higher annealing temperatures, and optical and dielectric properties are performed at lower annealing temperatures.

An optimal demodulation frequency band selection method based on spectral coherence fault energy factor for rolling bearing fault diagnosis

Identifying a frequency band with abundant fault information from the original vibration signal under the condition of complex background noise and the interference of other rotating parts is still problematic for envelope spectrum-based bearing diagnosis. To address this issue, a new indicator called spectral coherence fault energy factor (SCFE) to is proposed to select the optimal demodulation frequency band (ODFB) under complex noise interference for bearing fault diagnosis. Firstly, the fast spectral correlation (Fast-SC) is used to analyze the raw vibration signal and generate the Spectral Coherence (SCoh) image. Then, SCFE is employed to locate the initial center frequency from the whole carrier frequency, and the threshold function is used to optimize the center frequency and bandwidth adaptively to obtain ODFB. Finally, the envelope spectrum (ES) is used to identify bearing faults. The effectiveness of the developed method in identifying the fault related frequency band and bearing fault diagnosis is validated and compared using simulated signals with different interference noises. The results of experimental signals demonstrate that the developed indicator can efficiently evaluate bearing failure information, and the presented approach can accurately discriminate the failure-related frequency band and detect different bearing faults compared with the traditional methods.

Mapping dominant tree species of miombo woodlands in Western Tanzania using PlanetScope imagery

Mapping dominant tree species in miombo woodlands is essential for enhancing their monitoring and management. We evaluated PlanetScope imagery to map Julbernardia globiflora, Brachystegia spiciformis, and Pterocarpus tinctorius in Tongwe Forest Reserve, Tanzania. The study assessed the effectiveness of PlanetScope bands in discriminating tree species and investigated how different months/seasons influenced tree species classification. Optimal months (seasons) and spectral bands were selected using Principal Component loading, temporal pattern analysis, mean decrease in accuracy, and mean decrease Gini techniques. Random forest classification was employed for tree species classification, and accuracy was assessed using an error matrix. The study identified March, July, and September as optimal months for acquiring imagery, with effective bands including blue, green-1, green, yellow, red, and red-edge. July and September imagery in the dry season achieved higher overall accuracies of 65% and 69%, respectively, while March imagery in the wet season reached 55%. The highest overall accuracy of 72% was achieved using images from different seasons. Producer’s accuracy was highest for Brachystegia spiciformis (79%) and Julbernardia globiflora (95%), whereas Pterocarpus tinctorius had lower accuracy (25%). User’s accuracy varied with 74% for Brachystegia spiciformis, 70% for Julbernardia globiflora, and 67% for Pterocarpus tinctorius. Mapping accuracy was notably high for Brachystegia spiciformis and Julbernardia globiflora, reflecting their high sample size (dominance) and distinct phenology. The yellow and red bands were particularly effective in distinguishing miombo tree species demonstrating PlanetScope’s capability. Future research should focus on scaling up PlanetScope’s application for broad miombo tree species mapping.

Identification of varieties of wheat seeds based on multispectral imaging combined with improved YOLOv5

The identification of seed variety is important in wheat production because the growth and yield are highly related with its variety. Traditional identification methods for wheat seed varieties were suffered with time consuming and contamination. This study proposed a method for convenient identification of wheat seed varieties by using improved YOLOv5 combined with multispectral images. Three optimal spectral bands images including 405nm, 570nm and 890nm were selected to fuse new image from all 19 bands using Genetic algorithm and confusion matrix. The YOLOv5s model for wheat seed variety identification was improved by adding a convolutional block attention module (CBAM) and the identification model was developed with the fusion images. The identification performance of proposed method achieved an accuracy of 99.38% in testing set, which was better than traditional VOLOv5 with RGB images or with the multispectral images. Meanwhile, the evaluation indexes of the model such as P/%, R/%, F1/% and mAP/% were all higher than 90%, which showed that the method was suitable for identification of wheat seeds variety rapidly and non-destructively.

STAKgram: a method for optimal demodulation band selection in bearing fault diagnosis under complex interference

Abstract Within rotating machinery, bearings constitute critical and vulnerable components. When they operate in a compromised state, they generate periodic shock pulses. While spectral kurtosis (SK) has been demonstrated as an effective tool for pulse detection, in complex operating environments, the vibration signals of damaged bearings acquired through sensors often contain a variety of interfering pulses, such as ambient background noise, episodic single pulses, which cause SK to produce poor computational results. In this paper, an innovative bearing fault diagnostic method named subband trimmed average kurtogram (STAKgram) is proposed, aiming to minimize the effect of interfering signals on signal processing results, thus ensuring reliable diagnostic results even in complex interference environments. Firstly, the signal is segmented into N sub-signals by employing a sliding window method. Following, the subband kurtosis is calculated for every sub-signal. Ultimately, the trimmed average kurtosis of the respective sub-band is computed to construct the STAKgram, indicating the optimal demodulation frequency band. It is used for squared envelope demodulation analysis. By simulation analysis and the application of test signals obtained under conditions closely resembling authentic bearing operating conditions, the STAKgram method is proven to provide more accurate results for diagnosing bearing faults in complex interference.

Multi-Functional Silole Hole Transport Layer for Efficient and Stable Lead-Tin Perovskite and Tandem Solar Cells.

Despite high theoretical efficiencies and rapid improvements in performance, high-efficiency ≈1.2eV mixed Sn-Pb perovskite solar cells (PSCs) generally rely on poly(3,4-ethylenedioxythiophene) polystyrenesulfonate (PEDOT: PSS) as the hole transport layer (HTL); a material that is considered to be a bottleneck for long-term stability due to its acidity and hygroscopic nature. Seeking to replace PEDOT: PSS with an alternative HTL with improved atmospheric and thermal stability, herein, a silole derivative (Silole-COOH) tuned with optimal electronic properties and efficient carrier transport by incorporating a carboxyl functional group is designed, which results in an optimal band alignment for hole extraction from Sn-Pb perovskites and robust air and thermal stability. Thin films composed of the Silole-COOH exhibit superior conductivity and carrier mobility compared to PEDOT: PSS, in addition to reduced nonradiative quasi-Fermi-level splitting losses at the HTL/perovskite interface and improved quality of Sn-Pb perovskite. Replacement of PEDOT: PSS with Silole-COOH leads to 23.2%-efficient single-junction Sn-Pb PSCs, 25.8%-efficient all-perovskite tandems, and long operating stability in ambient air.

Novel discretized gravitational search algorithm for effective medical hyperspectral band selection

Medical hyperspectral imaging present a promising avenue for non-invasive diagnostic methods for diseases. Nonetheless, the sparsity of medical hyperspectral data within high-dimensional spaces introduces the “curse of dimensionality”, which diminishes the efficiency and accuracy of data processing efforts. Therefore, spectral dimensionality reduction emerges as an essential process in the analysis and utilization of MHSIs data. To retain the intrinsic properties of the spectral bands, an effective unsupervised band selection algorithm is proposed leveraging the gravitational search algorithm (GSA-UBS) to identify the optimal band subset. Taking into account the informational content and redundancy among candidate bands, a comprehensive evaluation criterion is established that incorporates a band distance matrix and an information entropy vector. Additionally, a straightforward discrete search strategy is developed that enables gravitational search algorithm to directly retrieve the original sequence numbers of the selected bands, bypassing the conventional 0–1 band weighting approach. The extensive evaluation of GSA-UBS on three publicly available invivo brain cancer MHSIs datasets and a remote sensing hyperspectral image demonstrates its superior performance compared to various state-of-the-art methods. The source code for GSA-UBS can be accessed at https://github.com/zhangchenglong1116/GSA_UBS.

Enhanced thermoelectric performance in p-type AgBiSe2 through carrier concentration optimization and valence band modification

Enhanced thermoelectric performance in p-type AgBiSe2 through carrier concentration optimization and valence band modification

A novel optimal resonance band selection method for wheelset-bearing fault diagnosis based on tunable-Q wavelet transform

The detection of faults in the wheelset-bearing system is crucial for guaranteeing the safety of train operations. The core is to extract the optimal resonance band (ORB) and repetitive transient impact signals from the collected axle box acceleration signals. Accordingly, a novel automatic fault detection method called the bidirectional iterative merging multi-Q tunable-Q wavelet transform (BIMMQTQWT) is proposed to address the issue that existing methods are vulnerable to background noise and irrelevant components. First, a series of band-pass filters with almost constant bandwidth are constructed by the improved multi-Q tunable-Q wavelet transform (IMQTQWT) derived from the fault characteristics. Second, the fault information contained in each sub-band coefficient is preliminarily estimated using the correlative envelope comprehensive indicator (CECI). Third, the ORBs are automatically selected using the maximum CECI based on a strategy called bidirectionally merging adjacent frequency bands (BIMFBs). Finally, Envelope demodulation based on the ORB is executed followed by identifying bearing faults. The effectiveness in detecting multiple wheelset-bearing faults of the proposed method is validated through simulation and bench experiment signals. And the superior performance of the proposed method is exhibited compared with the existing average infogram and resonance sparse decomposition.

Structural graph learning method for hyperspectral band selection

ABSTRACT Recently, graph learning-based hyperspectral band selection algorithms illustrate impressive performance for hyperspectral image (HSI) processing, whose goal is to select an optimal band combination containing less redundancy through the learned graph matrix. However, most of the previous methods work in single spectral domain, which neglects the rich image spatial information. Moreover, they typically model the graph matrix from a global perspective while ignoring the differences in spatial distribution that exist in diverse pixel and band regions. Based on the above considerations, to take full account of structure information in spatial and spectral domains, a structural graph learning method for hyperspectral band selection (SGLM) is designed. Specifically, SGLM constructs two matrices using the correlation between pixels and bands under the local perspective, which can capture the image structure information reasonably. Since the proposed method is modelled in both spatial and spectral dimensions, it is conducive to subsequent band subset generation task. Meanwhile, in order to guarantee local spatial consistency among bands, a Laplacian regularization term associated with the error matrix is introduced to the self-representation model. Additionally, considering that the importance of a band is consistent with its capability to reconstruct the entire band set, an adjacent bands reconstruction strategy is adopted to obtain the ultimate band combination, which can assess the significance of each band effectively. The comprehensive experimental analysis on four datasets demonstrates that the proposed SGLM model has outstanding superiority in comparison with several band selection algorithms.

Optimizing junction interface integrity between 3D/2D defect pyrochlore Bi1.8Fe0.2WO6 and g-C3N4 nanostructures: A novel multifunctional nanocomposite for enhanced photocatalytic and photo-electrocatalytic water splitting and water remediation applications

In this work, a simplistic composite fabrication of defect pyrochlore Bi1.8Fe0.2WO6 (BFWO) and g-C3N4 (g-CN) was carried out to augment the photocatalytic degradation kinetics and Hydrogen (H2) evolution rate. The structural confirmation through XRD and FTIR confirmed the successful formation of pristine g-C3N4, BFWO NPs, and their composites. FESEM micrographs revealed multilayered sheet-like formation for C3N4 whereas irregular cuboid-shaped structure for BFWO NPs. In the composite formation, the sheets tended to appear wrapped around the BFWO NPs. The UV-DRS absorbance spectra exhibited a highlighted increase in the absorbance region towards the visible light region following a decrease in the band gap. The photoluminescence lifetime studies revealed an enhanced lifetime of excitons to 6.21 ns for 50:50 (g-C3N4: BFWO) composite formation, whereas pristine g-CN displayed an average lifetime of about 4.79 ns. The degradation efficacy in the removal of RhB and MB from water revealed up to 100% and 95.5% removal rates in under 90 mins and 180 mins respectively using the 50:50 composite formation. Similarly, the H2 evolution rate was significantly improved and exhibited up to 370 μmol/h/g. The photo-electrochemical studies revealed a sharp charge separation of excitons for 50:50 (g-C3N4: BFWO) with a maximum photocurrent density of -0.54 μA/cm2 @ 0 V which is 3.6 times and 13.5 times higher than bare g-CN and BFWO. 1.33 V vs. RHE for OER kinetics and -0.09 V vs RHE for HER kinetics were the minimal onset potential exhibited for equal ratios of BFWO and g-CN. Coupled with the heightened charge separation, reduced recombination rate, and optimal band edge positions, the photocatalytic efficiency in the degradation of RhB and MB and water-splitting reactions were improved.

Single Crystal Sn-Based Halide Perovskites.

Sn-based halide perovskites are expected to be the best replacement for toxic lead-based counterparts, owing to their similar ionic radii and the optimal band gap for use in solar cells, as well as their versatile use in light-emitting diodes and photodetection applications. Concerns, however, exist about their stability under ambient conditions, an issue that is exacerbated in polycrystalline films because grain boundaries present large concentrations of defects and act as entrance points for oxygen and water, causing Sn oxidation. A current thriving research area in perovskite materials is the fabrication of perovskite single crystals, promising improved optoelectronic properties due to excellent uniformity, reduced defects, and the absence of grain boundaries. This review summarizes the most recent advances in the fabrication of single crystal Sn-based halide perovskites, with emphasis on synthesis methods, compositional engineering, and formation mechanisms, followed by a discussion of various challenges and appropriate strategies for improving their performance in optoelectronic applications.