Ultrahigh Carbon Research Articles

Tailoring O-Monodentate Adsorption of CO2 Initiates C-N Coupling for Efficient Urea Electrosynthesis with Ultrahigh Carbon Atom Economy.

The thermodynamically and kinetically sluggish electrocatalytic C-N coupling from CO2 and NO3 - is inert to initially take place while typically occurring after CO2 protonation, which severely dwindles urea efficiency and carbon atom economy. Herein, we report a single O-philic adsorption strategy to facilitate initial C-N coupling of *OCO and subsequent protonation over dual-metal hetero-single-atoms in N2-Fe-(N-B)2-Cu-N2 coordination mode (FeN4/B2CuN2@NC), which greatly inhibits the formation of C-containing byproducts and facilitates urea electrosynthesis in an unprecedented C-selectivity of 97.1 % with urea yield of 2072.5 μg h-1 mgcat. -1 and 71.9 % Faradaic efficiency, outperforming state-of-the-art electrodes. The carbon-directed antibonding interaction with Cu-B is elaborated to benefit single O-philic adsorption of CO2 rather than conventional C-end or bridging O,O-end adsorption modes, which can accelerate the kinetics of initiated C-N coupling and protonation. Theoretical results indicate that the O-monodentate adsorption pathway benefits the thermodynamics of the C-N coupling of *OCO with *NO2 and the protonation rate-determining step, which markedly inhibits CO2 direct protonation. This oriented strategy of manipulating reactant adsorption patterns to initiate a specific step is universal to moderate oxophilic transition metals and offers a kinetic-enhanced path for multiple conversion processes.

Tailoring O‐Monodentate Adsorption of CO2 Initiates C−N Coupling for Efficient Urea Electrosynthesis with Ultrahigh Carbon Atom Economy

AbstractThe thermodynamically and kinetically sluggish electrocatalytic C−N coupling from CO2 and NO3− is inert to initially take place while typically occurring after CO2 protonation, which severely dwindles urea efficiency and carbon atom economy. Herein, we report a single O‐philic adsorption strategy to facilitate initial C−N coupling of *OCO and subsequent protonation over dual‐metal hetero‐single‐atoms in N2−Fe−(N−B)2−Cu−N2 coordination mode (FeN4/B2CuN2@NC), which greatly inhibits the formation of C‐containing byproducts and facilitates urea electrosynthesis in an unprecedented C‐selectivity of 97.1 % with urea yield of 2072.5 μg h−1 mgcat.−1 and 71.9 % Faradaic efficiency, outperforming state‐of‐the‐art electrodes. The carbon‐directed antibonding interaction with Cu−B is elaborated to benefit single O‐philic adsorption of CO2 rather than conventional C‐end or bridging O,O‐end adsorption modes, which can accelerate the kinetics of initiated C−N coupling and protonation. Theoretical results indicate that the O‐monodentate adsorption pathway benefits the thermodynamics of the C−N coupling of *OCO with *NO2 and the protonation rate‐determining step, which markedly inhibits CO2 direct protonation. This oriented strategy of manipulating reactant adsorption patterns to initiate a specific step is universal to moderate oxophilic transition metals and offers a kinetic‐enhanced path for multiple conversion processes.

Ultrahigh carbon utilization in symbiotic biofilm-sludge denitrification systems using polymers as sole electron donors

The polymer-based denitrification system is an effective nitrate removal process for treating low carbon/nitrogen wastewater. However, in polymer denitrification systems, carbon used for the denitrification reaction is weakly targeted. Improving the efficiency of carbon utilization in denitrification is important to reduce carbon wastage. In this study, a symbiotic biofilm-sludge denitrification system was constructed using polycaprolactone as electron donors. Results show that the carbon release amount in 120 days was 85.32±0.46 g, and the unit mass of polycaprolactone could remove 1.55±0.01 g NO3−-N. Meaningfully, the targeted carbon utilization efficiency for denitrification could achieve 79%-85%. The quantitative results showed that the release of electron donors can be well matched to the demand for electron acceptors in the biofilm-sludge denitrification system. Overall, the symbiotic system can improve the nitrate removal efficiency and reduce the waste of carbon source.

Ultra high density carbon fiber derived from isotactic polypropylene fiber without skin‐core structure

AbstractPolypropylene (PP)‐based carbon fibers were prepared by sulfonation process of isotactic PP fibers with concentrated sulfuric acid, followed by stress‐less carbonization under nitrogen atmosphere. The stabilization behaviors of PP fiber under different sulfonation temperatures and time were discussed. The carbonization behavior of the stabilized PP fibers under different carbonization temperatures, as well as the mechanical performance of the obtained carbon fibers were investigated. The results indicated that linear PP molecule were effectively converted into thermally stable structure at higher temperature (≥130°C) in short time (2 h) through sulfonation‐desulfonation reaction, among which ordered graphite structure has been formed prior to the carbonization process. Meanwhile, the carbon fibers were considerably densified by increasing the sulfonation temperature and carbonization temperature, and a bulk density of 1.96 g/cm3 was achieved. Moreover, the temperature and time of the sulfonation process as well as the temperature of the carbonization process were regulated, and carbon fibers with tensile strength of 262.3 MPa was obtained, which was superior to that of 208.1 MPa for the linear low density polyethylene‐based carbon fibers reported previously. Isotactic PP was proved to be a promising candidate to develop carbon fibers with tunable graphite structure and mechanical performance.

Automatic Microstructural Classification of Ultrahigh Carbon Steel with Vision Transformers and Convolutional Neural Networks

Microstructural classification is key to better understanding of the relationships between the microstructures of carbon steels and their properties, as well as the development of automatic models to reliably and efficiently categorize the microstructures which can largely mitigate the burden of the human experts. Methods based on transfer learning with deep neural networks, such as convolutional neural networks (CNNs) have become established in automatic microstructural classification of carbon steels. However, vision transformers (ViTs), as a new class of deep learning algorithms, have recently emerged as a competitive alternative to CNNs in many other fields, but have not been considered in this field yet. In this study, the micrographs from an open-source dataset of ultrahigh carbon steel are analyzed by use of two CNNs (GoogLeNet and MobileNetV2) and two ViTs (ViT-B16 and ViT-L32). These deep neural networks are used as end-to-end classifiers to discriminate between different microstructures. Promising results were achieved by use of ViTs, as they performed as well as, or better than CNNs, though not by a large margin. These results suggest that ViTs can be a viable alternative to CNNs in the advancement of automatic microstructural classification.

Gas Phase-Heat Absorption-Condensate Phase Stepwise Flame Retardant Strategy to Prepare Coal Tar Pitch-Based Porous Carbon for Supercapacitor.

Porous carbon is widely used in energy storage-conversion systems, and the question of how to explore an efficient strategy for preparation is very significant. Herein, the flame retardant capability of (NH4 )2 SO4 /Mg(OH)2 that contains gas phase-heat absorption-condensate phase components is assisted to carbonize coal tar pitch in air and obtain the porous carbon. The mechanism of stepwise inflaming retarding is systematically investigated. In the carbonization process in a muffle furnace, (NH4 )2 SO4 decomposes releasing gases at below 400°C to act as the role of gas phase flame retardant. Mg(OH)2 starts to decompose at ≥ 400°C, and it has the effect of heat absorption and condensed phase flame retardation (MgSO4 and MgO). What's more, the flame retardant also serves as an N, S source and template. The obtained porous carbon possesses an ultrahigh carbon yield of 56.9wt.%, hierarchical pore structure, and multi-heteroatoms doping. It can still reach up to 244.7Fg-1 even loaded 20mg of active material. In addition, the (NH4 )2 SO4 /agar gel electrolyte is synthesized, and the fabricated flexible ammonium ion capacitor exhibits a superior energy density of 40.8Whkg-1 . This work uncovers a new way to construct porous carbon, which is expected to synthesize more carbon materials using other carbon sources.

Machine Learning Approaches for Classification of Ultra High Carbon Steel Micrographs

The aim of this investigation is to analyze the performance of several supervised machine learning algorithms for solving the automatic classification problem of steel image microstructures. We conducted an experiment using a public-domain dataset of Ultra High Carbon Steel Micrographs (UHCSM). This image database consists of a collection of scanning electron micrographs (SEM) taken from samples of a commercial roll-mill casting with a nominal carbon of 2%. Heat treatments such as annealing, water quenching, air and furnace cooling were performed on steel samples so primary microconstituents could be found in micrographs. Each of these microconstituents defines each of the categories of classification to be accomplished by machine learning algorithms. The heat treatments brought about 4 usable classes (sets of images) of primary microconstituents: pearlite, spheroidite, proeutectoid cementite network, pearlite containing spheroidite. All labeled images are prepared to improve models' accuracy in a preprocessing stage so that the image dataset is ready for feature extraction. In order to develop classification models, we put to the test distinct machine learning approaches by working with Matlab's classification learner application where we perform automated training to search for the best classification model type, including Decision Trees, Support Vector Machines (SVM), Discriminant Analysis, Nearest Neighbors, Naive Bayes, Ensemble k-NN, and Neural Network classification. For obtaining the features of the images (feature extraction) we choose the method of Bag-of-features with 400 words for the first experiment, and 327 words by removing less important features for a second experiment. The experimented models reached very different accuracy values on training, with SVM as the best classifier which gets 91.6% accuracy. We can conclude that classic machine learning algorithms solve the classification, but an accuracy improvement can be reached by investigating deep learning techniques.

Improving hydrogen-rich gas production from biomass catalytic steam gasification over metal-doping porous biochar

Biomass to green H2 is a new route to produce sustainable energy. This study aimed to boost H2-enriched gas production via gasification-catalytic steam reforming (GCSR) process of wheat straw (WS) over Ni, Fe, or Zn-doped carbon materials (MDCMs). Initially, steam injection rate (1 g/min) and residence time (15 min) was optimized based on the tradeoff between energy consumption and H2-rich gas generation. The largest gas yield (90.77 mmol/g) and the lowest H2 production efficiency (ƞ: 7.89 g CO2/g H2) were observed for WS-derived biochar. Clearly, it was found MDCMs were favorable for reducing CO2 production due to the strengthened CO2 reforming reactions catalyzed by metal active sites. A higher ƞ (6.72 g CO2/g H2) was achieved for Ni-doping biochar (Ni/C). Importantly, Ni/C showed the ultrahigh carbon conversion efficiency (99.47%) and great tar elimination performance. Overall, GCSR process over MDCMs is a newly promising way to valorize biomass into H2-rich gas.

Reversed CO2/C2H2 separation with ultrahigh carbon dioxide adsorption capacity in Zn-based pillared metal-organic frameworks

The removal of CO2 impurities from CO2/C2H2 gas mixtures to obtain high-purity acetylene used for manufacturing of various commodity chemicals, is very important for the chemical purifying of acetylene. However, CO2 and C2H2 exhibit analogous physical properties as well as molecular sizes causing their separation to become an industrially challenging process, and developing energy-efficient CO2-selective adsorbents is a class of materials of vast value. In this work, a pillared-layer ultramicroporous metal-organic framework (MOF) constructed from zinc (Ⅱ) and triazolium planar layers with oxalic acid, which has been termed as CALF-20, can preferentially absorb CO2 from C2H2. Unique pore environment and electrostatic effects enable this material foremost capture CO2. CALF-20 shows higher affinities towards CO2 than C2H2 based on heats of adsorption computed from single-component adsorption isotherms. Grand Canonical Monte Carlo simulations confirm the affinity interactions and multiple host-guest interactions between CO2 and CALF-20. Sorption experiments revealed that CALF-20 has excellent trapping volume with extremely high CO2 uptake capacity (140.2 cm3 cm−3).

Data augmentation in material images using the improved HP-VAE-GAN

Since the rapid development of computer vision relies heavily on large-scale labeled data and high-performance computing equipment, therefore, image recognition in small sample datasets faces several challenges, such as difficult to implement model training. In the field of materials research, the cost of collecting image data is relatively high. In order to solve the problem of insufficient image samples in material research, an improved HP-VAE-GAN is proposed to generate material images to achieve data augmentation. HP-VAE-GAN is a single sample generation model that consists of Patch-VAE and Patch-GAN. The improved HP-VAE-GAN introduces the attention mechanism into model. By adding CBAM (Convolutional Block Attention Module) to the encoder of Patch-VAE, the feature extraction and representation capabilities of the network are further improved. Use this model to train a single image, and then generate a certain number of samples to achieve the expansion of the training set. For the classification of ultrahigh carbon steel microstructure images, experiments show that the accuracy of classification model (MobileNet, ResNet50 and VGG16) trained with real images plus generated images is improved obviously. In addition, the effectiveness of the improved HP-VAE-GAN is verified by experiments on texture images similar to material images.

Direct production of olefins from syngas with ultrahigh carbon efficiency

Syngas conversion serves as a competitive strategy to produce olefins chemicals from nonpetroleum resources. However, the goal to achieve desirable olefins selectivity with limited undesired C1 by-products remains a grand challenge. Herein, we present a non-classical Fischer-Tropsch to olefins process featuring high carbon efficiency that realizes 80.1% olefins selectivity with ultralow total selectivity of CH4 and CO2 (<5%) at CO conversion of 45.8%. This is enabled by sodium-promoted metallic ruthenium (Ru) nanoparticles with negligible water-gas-shift reactivity. Change in the local electronic structure and the decreased reactivity of chemisorbed H species on Ru surfaces tailor the reaction pathway to favor olefins production. No obvious deactivation is observed within 550 hours and the pellet catalyst also exhibits excellent catalytic performance in a pilot-scale reactor, suggesting promising practical applications.

Ultrahigh carbon monoxide capture by novel protic cuprous-functionalized dicationic ionic liquids through complexation interactions

CO removal is a very important topic in the scientific and industrial communities. However, the CO capture performance of the currently reported absorbents is still unsatisfactory from an industrial perspective. Described herein have developed a series of novel protic functionalized dicationic ionic liquids (ILs) containing abundant cuprous sites for effective absorption and separation of CO. The physical properties of the prepared ILs were characterized, and the CO solubility was systematically determined. Strikingly, the solubility of CO in the designed IL [HBDMAEE-C6][Cu2Cl3]2 reaches as high as 1.440 mol/mol at 40 °C and 2.0 bar due to the accompanying phase change, which is the highest value reported so far. The interaction mechanism between [HBDMAEE-C6][Cu2Cl3]2 and CO was further elucidated by IR and NMR spectroscopy paired with Gaussian calculation, and the formation of Cu(CO)Cl complexes was confirmed. After ten consecutive absorption–desorption cycles, the CO uptake did not decrease significantly. As far as we know, this series of ILs show excellent CO capture performance and reversible regeneration performance. The strategy of multi-copper sites in this work provides important guidance for the design of novel solvents with promising applications in CO selective separation and storage.

High-yield and nitrogen self-doped hierarchical porous carbon from polyurethane foam for high-performance supercapacitors

Confronted with the environmental pollution and energy crisis issues, upcycling of waste plastics for energy-storage applications has attracted broad interest. Polyurethane (PUR) is a potential candidate for the preparation of N-doped carbon materials. However, its low carbon yield limits the utilization of PUR waste. In this study, PUR foam was converted into N-doped hierarchical porous carbon (NHPC) through an autogenic atmosphere pyrolysis (AAP)-KOH activation approach. An ultra-high carbon yield of 55.0% was achieved through AAP, which is more than 17 times the carbon yield of conventional pyrolysis of PUR. AAP converted 83.2% of C and 61.0% of N in PUR into derived carbon material. The high conversion rate and self-doping effect can increase the environmental and economic benefits of this approach. KOH activation significantly increased the specific surface area of carbon materials to 2057 m2 g−1 and incorporated hierarchical porous structure and O-containing functional groups to the carbon materials. The obtained NHPCs were applied to improve the performance of supercapacitors. The electrochemical measurement revealed that NHPCs exhibited a high specific capacitance of 342 F g−1 (133 F cm−3) at 0.5 A g−1, low resistance, and outstanding cycling stability. The energy density and power density of the supercapacitor were improved to 11.3 W h kg−1 and 250 W kg−1, respectively. This research developed a possible solution to plastic pollution and energy shortage.

Deep Learning Approaches to Image Texture Analysis in Material Processing

Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results.

Deformation behavior during hot torsion of an ultrahigh carbon steel containing 1.3 wt.% C

Abstract The torsion behavior of an ultrahigh carbon steel containing 1.3 wt.% C has been studied at high strain rates (2 – 26 s –1) and high temperatures (900 – 1200 °C). Adiabatic heating strongly affects the shape of the stress– strain curves. Grain size measurements at strains of ε = 2 and ε = 5 reveal that the austenite grain size does not change between these two strains and is finer than the initial austenite grain size. This is an indication that a recrystallization process took place in the interval of strains between ε peak and ε = 2 and that the deformation state associated to the strains of ε = 2 and ε = 5 can be considered as steady state. Stress–strain rate relations have been analysed for ε peak, ε = 2 and ε = 5. It has been found that data obtained at ε peak correlate well with a lattice diffusion-controlled dislocation creep process. On the other hand, analysis of the stress –strain rate relations found at the steady state indicated that the deformation occurs by the contribution of two independent mechanisms: grain boundary sliding controlled by pipe diffusion and slip creep controlled by lattice diffusion.

High deformation effects on the plasticity of ultra-high carbon steel wires

Steel wires under severe cold drawing deformation, develop high strength (5-6 GPa) and ductility. For these reasons it is relevant to increase the knowledge on the structural evolution and deformation mechanisms involved during wire drawing process, due to their critical applications such as bridges, cranes and tire cord. This paper presents a comparative study of steel wires (0.84%C) at different deformation stages. The product presents a normal behaviour under torsion test, the mentioned test is normally used to corroborate the wire aptitude. The main objective of the study is to increase the knowledge on the structural evolution after cold drawn considering the deformation mechanisms, cementite dissolution, and epsilon carbide precipitation. The microstructural study was carried out applying light and scanning electron microscopy (SEM-EBSD). The structural information was correlated with results of differential thermal analysis (DTA) and FactSage simulation. The structural study verified the presence of curling phenomenon in the wires. The interlaminar spacing (l) and the thickness of cementite lamellae in wires cold drawn from 8 mm up to 2 mm of diameter was determined. Finally, the dynamic strain aging, which is promoted by cementite destabilization and the precipitation of epsilon carbide was studied. Copyright © 2018 VBRI Press.

Heat treatment optimization of an automotive stamping die steel with low energy consumption

It has innovatively teat-treated an ultra-high carbon steel via a novel technique with low energy consumption for automotive stamping die application in this study. Formation mechanism of isothermal transformation product at low temperatures in an ultra-high carbon steel has been investigated by the means of X-ray diffraction (XRD) analysis and metallographic examination on the resulting microstructure, which is optimally designed to achieve an excellent combination of high impact toughness and high hardness. It is indicated that microstructure morphology of resulting bainite isothermally formed at temperatures below 200°C is observably different from water-quenched shear-transformed martensite; in the case of isothermal transformation above 200°C for a long time, it obtains bainite sheaf which is composed of slender bainitic ferrite plates separated by parallel parent phase films; bainite sheaf is achieved by nucleation and growth of new parallel bainite plates in the vicinity of both body sides of the already formed bainite plate.

Data-driven phase recognition of steels for use in mechanical property prediction

This paper develops a deep learning scheme of phase recognition for steel materials. A convolutional neural network classifier is established, such that the martensite phase, which has a substantial impact on the mechanical properties of steels, can be recognized from microstructure images and its volumetric fraction can also be estimated from multi-phase microconstituents. The testing results on an ultrahigh carbon steel dataset proved that the developed scheme has a rational phase recognition accuracy. The estimated martensite fraction can be used as an essential feature to predict the mechanical properties of materials in additive manufacturing.

On the Unique Microstructure and Properties of Ultra-High Carbon Bearing Steel Alloyed with Different Aluminum Contents

In this study, ultra-high-carbon steels with 1.4% carbon content alloyed with three different aluminum contents, 2.0%, 4.0% and 6.0%, were studied on their tempering stability and temperature resistance. The results showed that the addition of Al significantly enhanced the tempering stability and temperature resistance of ultra-high-carbon steel. The addition of Al inhibited the transformation of ε-carbide to cementite, suppressed the transition of martensite to ferrite and thus, endowed ultra-high carbon steels to maintain very high hardness during tempering within a wide range of temperature up to 500 °C. The present work provides a useful basis on which to develop bearing steel materials with low density and high hardness.

Instance Segmentation for Direct Measurements of Satellites in Metal Powders and Automated Microstructural Characterization from Image Data

We propose instance segmentation as a useful tool for image analysis in materials science. Instance segmentation is an advanced technique in computer vision which generates individual segmentation masks for every object of interest that is recognized in an image. Using an out-of-the-box implementation of Mask R-CNN, instance segmentation is applied to images of metal powder particles produced through gas atomization. Leveraging transfer learning allows for the analysis to be conducted with a very small training set of labeled images. As well as providing another method for measuring the particle size distribution, we demonstrate the first direct measurements of the satellite content in powder samples. After analyzing the results for the labeled data dataset, the trained model was used to generate measurements for a much larger set of unlabeled images. The resulting particle size measurements showed reasonable agreement with laser scattering measurements. The satellite measurements were self-consistent and showed good agreement with the expected trends for different samples. Finally, we present a small case study showing how instance segmentation can be used to measure spheroidite content in the UltraHigh Carbon Steel DataBase, demonstrating the flexibility of the technique.