Edge Degradation Research Articles

An improved method for upscaling 2D images using adaptive edge sharpening and an optimized directional anisotropic diffusion filter

Image upscaling employs various polynomial techniques, including bicubic and Lanczos, to minimize computing load for various real-time applications. Polynomial interpolation, on the other hand, results in blurring artifacts in high resolution (HR) images due to edge degradation. Many other interpolation approaches, including edge-based and dictionary learning-based schemes, result in blurring in the image’s high frequency (HF) region. As a result, a novel post-processing technique is proposed that uses the Lanczos method to upscale the low resolution (LR) image. To eliminate blur along the image’s edge, the edge component of the interpolated image is retrieved. Adaptive sharpening is used to make the edges more prominent, as predicting pixels in the high variance region is challenging. After the edge is extracted, the smooth region is filtered with an optimized directional anisotropic diffusion (ODAD) filter to preserve texture details and geometric regularity. The sharpened edge image is aggregated with the ODAD filtered image to generate an improved HR (IHR) image. The IHR is then scaled up and down to reduce blurring even further, resulting in a blurred HR image (BHR). The residual image is obtained by subtracting the BHR from the IHR images, which represent the undetected HF. Finally, the residual image and the IHR image are combined to generate the final restored image, which includes all prediction loss details. The results of the experiments show that the proposed algorithm outperforms existing methodologies, particularly in terms of visuality and parameter-wise measurement.

Opensource machine learning metamodels for assessing blade performance impairment due to general leading edge degradation

Blades leading edge erosion can significantly reduce annual energy production of wind turbines. Accurate estimates of the resulting blade performance impairment are paramount to predict the resulting energy losses and enable cost-informed decisions on optimal maintenance and operational strategies, maximizing energy production and reducing maintenance costs. Computational Fluid Dynamics (CFD) is a robust approach for predicting the performance losses due to LEE. However, the impact of the damage on blade aerodynamics varies depending on damage pattern, extent and location. Therefore, direct CFD simulation of a sufficiently general set of damaged blades is computationally not viable in industrial applications, since the energy loss assessment needs to be performed for hundreds of turbines at many times of the wind farm operation. To address this issue, previous studies showed how CFD can be used to train machine learning metamodels of the perfomance of damaged blade sections, enabling the definition of multi-fidelity energy loss prediction systems. This study presents improved metamodels, using validated CFD to generate training datasets that cover a more general and wider range of erosion patterns, from low-amplitude roughness to severe grooves. In order to provide the industry with additional erosion geometry-linked tools for estimating energy yield losses, and foster further research and development in this area, the developed meta-models have been made available online with unrestricted access.

Convolution-transformer blend pyramid network for underwater image enhancement

Underwater images suffer from different types of degradation, where color degradation occurs in the spatial domain and edge degradation in the frequency domain. The high-quality underwater image enhancement represents a crucial milestone in advancing computer vision systems tailored for marine environments. This foundational endeavor encompasses a wide array of applications in computer vision tasks, including underwater inspection, underwater archaeology, and environmental monitoring. However, current convolutional neural network (CNN)-based pyramid frameworks primarily focus on capturing local features, often overlooking the significance of global semantic features that play a crucial role in understanding underwater scenes. Moreover, these frameworks handle spatial and frequency features independently, failing to enhance images by exploring correlation among domain-specific attributes for enabling information consistency. Besides, optimizing the model using a loss function with the same domain attributes from the ground truth may not lead to a better generalization ability. To solve these problems, we propose a new Convolution-Transformer Blend Pyramid Network (CTPN), which consists of a spatial branch and several frequency branches. The CTPN has four key components: a Swin transformer encoder, a CNN-Transformer aggregated encoder–decoder (CTED) and a blend pyramid framework. The Swin transformer encoder is employed to capture global semantic features, benefiting from its ability to extract long-range and global dependencies among features. The CTED fuses local features captured by CNN layers and global semantic features captured by the Swin transformer encoder in the spatial branch, with the help of the Cross-Model Fusion Module (CFM) and Skip-Aggregation Module (SAM). Subsequently, a blend pyramid framework is designed which not only progressively expands the transformed information of the previous domain branch to the current domain branch via the CTED-based refining operation, but also utilizes the proposed Domain Affinity Block (DAB) to explore the connection between domain attributes, ensuring information consistency. The experimental results demonstrate that the proposed method outperforms existing underwater image enhancement methods quantitatively and qualitatively.

Generated realistic noise and rotation-equivariant models for data-driven mesh denoising

3D mesh denoising is a crucial pre-processing step in many graphics applications. However, existing data-driven mesh denoising models, primarily trained on synthetic white noise, are less effective when applied to real-world meshes with the noise of complex intensities and distributions. Moreover, how to comprehensively capture information from input meshes and apply suitable denoising models for feature-preserving mesh denoising remains a critical and unresolved challenge. This paper presents a rotation-Equivariant model-based Mesh Denoising (EMD) model and a Realistic Mesh Noise Generation (RMNG) model to address these issues. Our EMD model leverages rotation-equivariant features and self-attention weights of geodesic patches for more effective feature extraction, thereby achieving SOTA denoising results. The RMNG model, based on the Generative Adversarial Networks (GANs) architecture, generates massive amounts of realistic noisy and noiseless mesh pairs data for data-driven mesh denoising model training, significantly benefiting real-world denoising tasks. To address the smooth degradation and loss of sharp edges commonly observed in captured meshes, we further introduce varying levels of Laplacian smoothing to input meshes during the paired training data generation, endowing the trained denoising model with feature recovery capabilities. Experimental results demonstrate the superior performance of our proposed method in preserving fine-grained features while removing noise on real-world captured meshes.

A multimodal feature fusion image dehazing method with scene depth prior

AbstractCurrent dehazing networks usually only learn haze features in a single‐image colour space and often suffer from uneven dehazing, colour, and edge degradation when confronted with different scales of ground objects in the depth space of the scene. The authors propose a multimodal feature fusion image dehazing method with scene depth prior based on a decoder–encoder backbone network. The multimodal feature fusion module was first designed. In this module, affine transformation and polarized self‐attention mechanism are used to realize the fusion of image colour and depth prior feature, to improve the representation ability of the model for different scale ground haze feature in‐depth space. Then, the feature enhancement module (FEM) is added, and deformable convolution and difference convolution methods are used to enhance the representation ability of the model for the geometric and texture feature of the ground objects. The publicly available dehazing datasets are used for comparison and ablation experiments. The results show that compared with the existing classical dehazing networks, the peak signal‐to‐noise ratio (PSNR) and SSIM of the authors’ proposed method have been significantly improved, have a more uniform dehazing effect in different depth spaces, and maintain the colour and edge details of the ground objects very well.

Updating forest road networks using single photon LiDAR in northern Forest environments

Abstract Knowledge about the condition and location of forest roads is important for forest management. Coupling accurate forest road information with planning and conservation strategies supports forest resource management. In Canada, spatial data of forestry road networks are available provincially; however, they lack spatial accuracy, and up-to-date information on key attributes such as road width is missing. In this study, we apply a novel approach to update forest road networks and characterize road conditions in Ontario’s Boreal and Great Lakes—St. Lawrence (GLSL) Forest regions. We use airborne laser scanning (ALS), to facilitate the identification of forest roads across densely forested landscapes. We categorized roads into four classes based on driveable width, edge vegetation, as well as surface and edge degradation as derived from high-density Single Photon LiDAR (SPL) data. Using a novel road extraction method, we produced a road probability raster and map road centerlines. We validated road location and attribute information using Global Navigation Satellite System (GNSS) ground truth data in two Ontario forest management units, in the boreal forest and the GLSL. Road segments in some regions have been altered to account for land cover changes, such as flooding or fallen trees. In other situations, the road path may deviate from the planned layout of the road, which is not always followed in the field. Our results highlight inaccuracies in the existing road networks, with 30 per cent of ‘Full access’ roads and 29 per cent of ‘Partial access’ roads being undriveable by standard vehicles and 45 per cent of ‘Status unknown’ roads, which make up 48 per cent of the pre-existing network, being driveable by standard vehicles. Results show that the average positional accuracy of updated road centerlines is 0.4 m, and the average road width error is 2 m. The production of spatially accurate forest road networks and road attribute information is important for characterizing large road networks for which often minimal information is available.

Towards biodegradable conducting polymers by incorporating seaweed cellulose for decomposable wearable heaters.

Thermotherapy shows significant potential for pain relief and enhanced blood circulation in wildlife rehabilitation, particularly for injured animals. However, the widespread adoption of this technology is hindered by the lack of biodegradable, wearable heating pads and concerns surrounding electronic waste (E-waste) in natural habitats. This study addresses this challenge by investigating an environmentally-friendly composite comprising poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS), seaweed cellulose, and glycerol. Notably, this composite exhibits remarkable biodegradability, losing half of its weight within one week and displaying noticeable edge degradation by the third week when placed in soil. Moreover, it demonstrates impressive heating performance, reaching a temperature of 51 °C at a low voltage of 1.5 V, highlighting its strong potential for thermotherapy applications. The combination of substantial biodegradability and efficient heating performance offers a promising solution for sustainable electronic applications in wildlife rehabilitation and forest monitoring, effectively addressing the environmental challenges associated with E-waste.

Face Anti-Spoofing Method Based on Residual Network with Channel Attention Mechanism

The face recognition system is vulnerable to spoofing attacks by photos or videos of a valid user face. However, edge degradation and texture blurring occur when non-living face images are used to attack the face recognition system. With this in mind, a novel face anti-spoofing method combines the residual network and the channel attention mechanism. In our method, the residual network extracts the texture differences of features between face images. In contrast, the attention mechanism focuses on the differences of shadow and edge features located on nasal and cheek areas between living and non-living face images. It can assign weights to different filter features of the face image and enhance the ability of network extraction and expression of different key features in the nasal and cheek regions, improving detection accuracy. The experiments were performed on the public face anti-spoofing datasets of Replay-Attack and CASIA-FASD. We found the best value of the parameter r suitable for face anti-spoofing research is 16, and the accuracy of the method is 99.98% and 97.75%, respectively. Furthermore, to enhance the robustness of the method to illumination changes, the experiment was also performed on the datasets with light changes and achieved a good result.

Impact of finite bandwidth on PRBS-modulated optical spectra.

Applying a pseudo-random binary sequence (PRBS) to phase modulators is a recent development in the broadening of optical spectra of laser sources to defeat stimulated Brillouin scattering (SBS). The theoretical underpinning of this method relies on alternating the phase of the optical signal between its native value and an out-of-phase value (i.e., imposing a binary phase shift of 0 or π in a pseudo random manner) to prevent coherent buildup of the SBS acoustic grating. In real physical systems, realizing such a binary shift is impossible due to the finite response times of the electronics and electro-optic components. The influence of these effects is investigated in this work, specifically the finite bandwidth of the electronic PRBS generator and the frequency-dependent response of the phase modulator, on the resultant temporal waveforms of the PRBS signal and its RF optical spectra. It is found that the optimal SBS suppression in real systems is achieved when the phase modulator is driven at a voltage beyond V π, even though driving at V π has been deemed as ideal. Moreover, both the SBS suppression and spectral broadening are always weaker than what is predicted by analytical results since any degradation of the sharp edges of the PRBS signal result in loss of high-frequency content in the RF spectrum of the PRBS signal. Lastly, it is noticed that the typical ways of measuring spectral width are not relevant when applied to the complex PRBS RF optical spectrum. An 'equivalent spectral width' is defined to accurately quantify the correlation between spectral width an SBS suppression.

Adaptive Image Denoising Method Based on Diffusion Equation and Deep Learning

Effective noise removal has become a hot topic in image denoising research while preserving important details of an image. An adaptive threshold image denoising algorithm based on fitting diffusion is proposed. Firstly, the diffusion coefficient in the diffusion equation is improved, and the fitting diffusion coefficient is established to overcome the defects of texture detail loss and edge degradation caused by excessive diffusion intensity. Then, the threshold function is adaptively designed and improved so that it can automatically control the threshold of the function according to the maximum gray value of the image and the number of iterations, so as to further preserve the important details of the image such as edge and texture. A neural network is used to realize image denoising because of its good learning ability of image statistical characteristics, mainly by the diffusion equation and deep learning (CNN) algorithm as the foundation, focus on the effects of activation function of network optimization, using multiple feature extraction technology in-depth networks to study the characteristics of the input image richer, and how to better use the adaptive algorithm on the depth of diffusion equation and optimization backpropagation learning. The training speed of the model is accelerated and the convergence of the algorithm is improved. Combined with batch standardization and residual learning technology, the image denoising network model based on deep residual learning of the convolutional network is designed with better denoising performance. Finally, the algorithm is compared with other excellent denoising algorithms. From the comparison results, it can be seen that the improved denoising algorithm in this paper can also improve the detail restoration of denoised images without losing the sharpness. Moreover, it has better PSNR than other excellent denoising algorithms at different noise standard deviations. The PSNR of the new algorithm is greatly improved compared with the classical algorithm, which can effectively suppress the noise and protect the image edge and detail information.

An improved Image Interpolation technique using OLA e-spline

Image upscaling aims to increase the resolution and size of a low resolution (LR) image in order to generate a high resolution (HR) image of high frequency (HF). There are several polynomial methods for obtaining a sharpened, upscaled HR image. The interpolated pixel is measured using a weighted average of the neighboring pixels within the image grid that blur at HF regions in these methods. Edge degradation is also caused by other edge-directed and learning-based upscaling methods, which produce blurring artifacts. A novel approach is proposed to fill these gaps. Using the concept of unsharp masking (USM), the LR image is blurred adaptively based on the region’s local variance. The sharpened high pass filtered (HPF) image is then obtained by subtracting the adaptively blurred image from the LR image. According to USM, the HPF image is combined with the LR image via a gain factor optimized using the cuckoo search (CS) algorithm. To compensate for the loss caused by upscaling, this pre-processing step is performed prior to interpolation. Aside from that, the edge of the B-spline interpolated image is detected and expanded. Edge expansion of the upscaled image is performed to further restore the HF details and reduce zigzag artifacts introduced by upscaling while also preserving the edge boundary. The proposed method outperforms the Lanczos, Bicubic, and Bilinear schemes in terms of peak signal to noise ratio (PSNR) gain of 3.475, 8.3839, and 8.075 dB, respectively. In terms of performance, this method outperforms state-of-the-art techniques both objectively (PSNR and SSIM) and subjectively (visual quality).

Modeling cutting edge degradation by chipping in micro-milling

The cutting edges of micro-milling tool undergo degradation with time, in the form of continuous abrasion and intermittent chipping. As a result, the cutting tool looses its original profile and consequently affects the quality of molds and dies manufactured using micro-milling process. This work therefore focuses on understanding the physics and mechanism of tool wear in micro-milling as function of tool life, and to model the transient degradation of cutting tool, which was not investigated in-depth in the literature. Extensive micro-milling experiments were done till the end of tool life, to investigate cutting edge degradation. Further, analytical as well as empirical models were developed to predict continuous abrasion interrupted by chipping, and to determine cutting edge profiles as a function of tool life. The abrasion wear model involved modifying Archard’s equation and evaluating tool wear coefficient empirically using a novel pin-on-disk device. The interrupted chipping wear phenomenon during micro-milling was identified by locating sudden drop in resultant forces on the tool. The chipping wear was then modeled using the dimensions of the chipped portions, extracted from the tool images captured in-situ. It was observed from the experiments that the length (C) of the chipped portions of the cutting edges was between 15.76 to 96 μm, whereas, their width (H) was between 7 to 29 μm, and they were found to have a positive correlation. The chipping load estimated from the resultant force, and the load inclination angle evaluated from the chipping model were found to have an inverse correlation, and varied between 1.23 to 3.75 N, and 69 to 25° respectively. The proposed abrasion and chipping wear model jointly gave transient progression of total volumetric tool wear area, and consequent cutting edge profiles over the tool-life cycle. The proposed tool wear model predicted the total wear within 9.3% of the experimental values.

Lateral thermokarst patterns in permafrost peat plateaus in northern Norway

Abstract. Subarctic peatlands underlain by permafrost contain significant amounts of organic carbon. Our ability to quantify the evolution of such permafrost landscapes in numerical models is critical for providing robust predictions of the environmental and climatic changes to come. Yet, the accuracy of large-scale predictions has so far been hampered by small-scale physical processes that create a high spatial variability of thermal surface conditions, affecting the ground thermal regime and thus permafrost degradation patterns. In this regard, a better understanding of the small-scale interplay between microtopography and lateral fluxes of heat, water and snow can be achieved by field monitoring and process-based numerical modeling. Here, we quantify the topographic changes of the Šuoššjávri peat plateau (northern Norway) over a three-year period using drone-based repeat high-resolution photogrammetry. Our results show thermokarst degradation is concentrated on the edges of the plateau, representing 77 % of observed subsidence, while most of the inner plateau surface exhibits no detectable subsidence. Based on detailed investigation of eight zones of the plateau edge, we show that this edge degradation corresponds to an annual volume change of 0.13±0.07 m3 yr−1 per meter of retreating edge (orthogonal to the retreat direction). Using the CryoGrid3 land surface model, we show that these degradation patterns can be reproduced in a modeling framework that implements lateral redistribution of snow, subsurface water and heat, as well as ground subsidence due to melting of excess ice. By performing a sensitivity test for snow depths on the plateau under steady-state climate forcing, we obtain a threshold behavior for the start of edge degradation. Small snow depth variations (from 0 to 30 cm) result in highly different degradation behavior, from stability to fast degradation. For plateau snow depths in the range of field measurements, the simulated annual volume changes are broadly in agreement with the results of the drone survey. As snow depths are clearly correlated with ground surface temperatures, our results indicate that the approach can potentially be used to simulate climate-driven dynamics of edge degradation observed at our study site and other peat plateaus worldwide. Thus, the model approach represents a first step towards simulating climate-driven landscape development through thermokarst in permafrost peatlands.

Experimental and numerical study of high frequency superconducting air-core transformer

Although many researches on superconducting air-core transformers have been carried out, it is mainly on the power frequency situation. The high-frequency characteristics of superconducting air-core transformers are still unclear. In this paper, we applied experimental and finite element simulation approach to study the operation and loss characteristics of high frequency superconducting air-core transformer made by YBa2Cu3O7−δ (YBCO) tapes. The numerical model established is based on the H formula and the E–J power law, and the edge degradation and anisotropy of the critical current density of the tape are considered in the model. The results show that the operating characteristics of superconducting transformers are significantly different from those of conventional transformers, and their operating characteristics have strong frequency and power dependence. The obtained findings can provide a theoretical basis for the design of high-frequency resonant transformers.

Electric bias-induced edge degradation of few-layer MoS2 devices

In this work, we experimentally investigate the effects of electric bias on the degradation of few-layer MoS2 back-gated field-effect transistors in ambient air. The devices were fabricated using mechanically exfoliated MoS2 flakes, which were transferred to a Si/SiO2 substrate by a PDMS-based transfer. We report an accelerated electric bias-induced degradation of the devices under investigation and used optical and scanning electron microscopy (SEM) to monitor changes of the morphology of the MoS2 channel. In particular, we found a linear dependency of the degradation on the electric field between the Ti/Au source and drain contacts. In addition, we identify four regions in which morphological changes occur, of which the edges of the MoS2 channel are most affected.

Accounting for Greenhouse Gas Emissions from Forest Edge Degradation: Gold Mining in Guyana as a Case Study

Background and Methods: Degradation of forests in developing countries results from multiple activities and is perceived to be a key source of greenhouse gas emissions, yet there are not reliable methodologies to measure and monitor emissions from all degrading activities. Therefore, there is limited knowledge of the actual extent of emissions from forest degradation. Degradation can be either in the forest interior, with a repeatable defined pattern within areas of forest, as with timber harvest, or on the forest edge and immediately bounding areas of deforestation. Forest edge degradation is especially challenging to capture with remote sensing or to predict from proxy factors. This paper addresses forest edge degradation and: (1) proposes a low cost methodology for assessing forest edge degradation surrounding deforestation; (2) using the method, provides estimates of gross carbon emissions from forest degradation surrounding and caused by alluvial mining in Guyana, and (3) compares emissions from mining degradation with other sources of forest greenhouse gas emissions. To estimate carbon emissions from forest degradation associated with mining in Guyana, 100 m buffers were located around polygons pre-mapped as mining deforestation, and within these buffers rectangular transects were established. Researchers collected ground data to produce estimates of the biomass damaged as a result of mining activities to apply to the buffer area around the mining deforestation. Results: The proposed method to estimate emissions from forest edge degradation was successfully piloted in Guyana, where 61% of the transects lost 10 Mg C ha−1 or less in trees from mining damage and 46% of these transects lost 1 Mg C ha−1 or less. Seventy percent of the damaged stems and 60% of carbon loss occurred in the first 50 m of the transects. The median loss in carbon stock from mining damage was 2.2 Mg C ha−1 (95% confidence interval: 0.0–10.2 Mg C ha−1). The carbon loss from mining degradation represented 1.0% of mean total aboveground carbon stocks, with emissions from mining degradation equivalent to ~2% of all emissions from forest change in Guyana. Conclusions: Gross carbon emissions from forest degradation around mining sites are of little significance regardless of persistence and potential forest recovery. The development of cost- and time-effective buffers around deforestation provides a sound approach to estimating carbon emissions from forest degradation adjacent to deforestation including surrounding mining. This simple approach provides a low-cost method that can be replicated anywhere to derive forest degradation estimates.

Multimodal medical image fusion via laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy

BackgroundIn recent years, numerous fusion algorithms have been proposed for multimodal medical images. The Laplacian pyramid is one type of multiscale fusion method. Although the pyramid-based fusion algorithm can fuse images well, it has the disadvantages of edge degradation, detail loss and image smoothing as the number of decomposition layers increase, which is harmful for medical diagnosis and analysis. MethodThis paper proposes a medical image fusion algorithm based on the Laplacian pyramid and convolutional neural network reconstruction with local gradient energy strategy, which can greatly improve the edge quality. First, multimodal medical images are reconstructed through convolutional neural network. Then, the Laplacian pyramid is applied in the decomposition and fusion process. The optimal number of decomposition layers is determined by experiments. In addition, a local gradient energy fusion strategy is utilized to fuse the coefficients in each layer. Finally, the fused image is output through Laplacian inverse transformation. ResultsCompared with existing algorithms, our fusion results represent better vision quality performance. Furthermore, our algorithm is considerably superior to the compared algorithms in objective indicators. In addition, in our fusion results of Alzheimer and Glioma, the disease details are much clearer than those of compared algorithms, which can provide a reliable basis for doctors to analyze disease and make pathological diagnoses.

Towards intelligent CFRP composite machining: Surface analysis methods and statistical data analysis of machined fibre laminate surfaces

Many carbon fibre reinforced polymer composite parts need to be edged trimmed before use to ensure both geometry and mechanical performance of the part edge matches the design intent. Measurement and control of machining induced surface damage of composite material is key to ensuring the part retains its strength and fatigue properties. Typically, the overall surface roughness of the machined face is taken to be an indicator of the amount of damage to the surface, and it is important that the measurement and prediction of surface roughness is completed reliably. It is known that the surface damage is heavily dependent on the fibre orientation of the composite and cutting tool edge condition. This research has developed a new ply-by-ply surface roughness measurement methods using optical focus variation surface analysis and image segmentation for calculating areal surface roughness parameters of a machined carbon fibre composite laminate. Machining experiments have been completed using a polycrystalline diamond edge trimming tool at increasing levels of cutting edge radius. Optical surface measurement and µ-CT scanning have been used to assess machining induced surface and sub-surface defects on individual fibre orientations. Statistical analysis has been used to assess the significance of machining parameters on Sa (arithmetic mean height of area) and Sv (areal magnitude of maximum valley depth) areal roughness parameters, on both overall roughness and ply-by-ply fibre orientations. Empirical models have been developed to predict surface roughness parameters using statistical methods. It has been shown that cutting edge degradation, fibre orientation and feed rate will significantly affect the cutting mechanism, machining induced surface defects and surface roughness parameters.

Hysteresis and eddy‐current losses in electrical steel utilising edge degradation due to cutting effects

AbstractCutting of electrical steel sheets typically deteriorates the permeability and increases the iron loss close to the cutting edges. We estimated iron losses in the cross‐section of electrical steel sheets by numerically solving the 1‐D and 2‐D eddy‐current distributions while accounting for static magnetic behaviour with a hysteresis model. The magnetization curves in the cross‐section are defined using a continuous local material model, making them dependent on the distance from the cut edge by a degradation profile. Damaged and undamaged hysteresis loops were identified by measurements of different wide strips of M400‐50A steel sheets. The eddy‐current distributions were solved when the strips of different widths were excited with sinusoidal average flux densities at different frequencies. It was found that the cutting degradation also affects the eddy‐current loss particularly around 1.0 T. The exact shape of the degradation profile was found to be less significant while the increase of excess losses is significant for the overall loss estimation.

Freezing Titanium Carbide Aqueous Dispersions for Ultra-long-term Storage.

Two-dimensional titanium carbide (Ti3C2Tx), or MXene, is a new nanomaterial that has attracted increasing interest due to its metallic conductivity, good solution processability, and excellent energy storage performance. However, Ti3C2Tx MXene flakes suffer from degradation through oxidation due to prolonged exposure to oxygenated water. Preventing the occurrence of oxidation, i.e., the formation of TiO2 particles, was found to be crucial in maintaining MXene quality. In the present work, we found that freezing aqueous MXene dispersions at a low temperature can effectively prevent the formation of TiO2 nanoparticles at the flake edge, which is known as the early stage of oxidation. The Ti3C2Tx flakes in frozen dispersion remain consistent in morphology and elemental composition for over 650 days, compared with freshly synthesized MXene, which in contrast exhibits flake edge degradation within two days when stored at room temperature. This result suggests that freezing a MXene dispersion dramatically postpones the oxidation of MXene flakes and that the stored MXene dispersion can be treated as freshly prepared MXene. This work not only fundamentally fulfilled the study on temperature dependence of MXene oxidation but has also demonstrated a simple method to extend the shelf life of MXene aqueous dispersion to years, which will be a cornerstone for large-scale production of MXene and ultimately benefit the research on MXenes.