Linear low-density polyethylene (LLDPE) waste is difficult to upcycle into more valuable carbon materials because it tends to completely decompose into small molecules during thermal processing. In this work, LLDPE is upcycled into a high quality turbostratic graphene using a pre-treatment step to oxidatively crosslink the polymer with the assistance of solid additives (KCl and K2CO3) that improve crosslinking by increasing the effective surface area of the polymer melt during processing. After this pretreatment step, the crosslinked polymer could then be carbonized and catalytically graphenized between 400-950 degrees C without decomposition of the polymer feedstock. The LLDPE derived graphene (LLDPE-G) obtained from this process has a Brunauer-Emmett-Teller (BET) specific surface area, up to 1800 m(2) g(-1) and average Raman I-D/I-G and I-2D/I-G ratios of 0.85 and 0.57, respectively, indicating high quality graphene. When used as an electrode material in symmetric supercapacitors, LLDPE-G possesses a specific capacitance up to 175 Fg(-1) at a mass loading of 20 mgcm(-2), which is two times the commercial requirement, yielding an areal capacitance of 3.5 Fcm(-2). Moreover, LLDPE-G exhibits exceptional cycling stability with a capacitance retention of 95.8 % after 100,000 cycles at a current density of 4.0 Ag-1. Additionally, the KCl and K2CO3 solids are recycled and reused over 3 complete reaction cycles to make new LLDPE-G with the material quality and electrocapacitive performance retained and verified after each cycle. Our approach creates new opportunities for upcycling waste LLDPE and other varieties of polyethylene into a higher value graphene used for electrochemical energy storage applications.

Despite graphene being considered an ideal supercapacitor electrode material, its use in commercial devices is limited because few methods exist to produce high-quality graphene at a large scale and low cost. A simple method is reported to synthesize 3D graphene by graphenization of coal tar pitch with a K2CO3 catalyst. This produces 3D graphenes with high specific surface areas up to 2113 m2 g-1 and exceptional crystallinity (Raman ID/IG as low as approximate to 0.15). The material has an outstanding specific capacitance of 182.6 F g-1 at a current density of 1.0 A g-1. This occurs at a mass loading of 30 mg cm-2 which is 3 times higher than commercial requirements, yielding an ultra-high areal capacitance of 5.48 F cm-2. The K2CO3 is recycled and reused over 10 cycles with material quality and electrocapacitive performance of 3D graphene retained and verified after each cycle. The synthesis method and resulting electrocapacitive performance properties create new opportunities for using 3D graphene more broadly in practical supercapacitor devices. Three-dimensional graphene with high specific surface areas up to 2113 m2 g-1 and exceptional crystallinity is synthesized by catalytic graphenization. Its application as electrode materials for ultra-high mass loading supercapacitors exhibits an outstanding specific capacitance of 182.6 F g-1 and an ultra-high areal capacitance of 5.48 F cm-2 at a current density of 1.0 A g-1. image

Carbon-based nanomaterials, such as carbon nanoplatelets, graphene oxide, and carbon quantum dots, have many possible end-use applications due to their ability to impart unique mechanical, electrical, thermal, and optical properties to cement composites. Despite this potential, these materials are rarely used in the construction industry due to high material costs and limited data on performance and durability. In this study, domestic coal is used to fabricate low-cost carbon nanomaterials that can be used economically in cement formulations. A range of chemical and physical processing approaches are employed to control the size, morphology, and chemical functionalization of the carbon nanomaterial, which improves its miscibility with cement formulations and its impact on mechanical properties and durability. At loadings of 0.01 to 0.07 wt.% of coal-derived carbon nanomaterial, the compressive and flexural strength of cement samples are enhanced by 24% and 23%, respectively, in comparison to neat cement. At loadings of 0.02 to 0.06 wt.%, the compressive and flexural strength of concrete composites increases by 28% and 21%, respectively, in comparison to neat samples. Additionally, the carbon nanomaterial additives studied in this work reduce cement porosity by 36%, permeability by 86%, and chloride penetration depth by 60%. These results illustrate that low-loadings of coal-derived carbon nanomaterial additives can improve the mechanical properties, durability, and corrosion resistance of cement composites.

Limited by the imaging dynamic range of the camera, the phenomenon of over-exposure and over-dark often occurs in the 3D measurement of strong reflective sheet metal parts, resulting in incomplete measurement result. One of existing methods such as multiple exposure can measure most of the visible area under a single viewpoint, but the visible area with too small or too large incidence angle still cannot be measured. To solve this problem, in this paper, a method of viewpoint planning for sheet metal parts with strong reflection is proposed. The method introduces the surface reflection model of reflective sheet metal parts into viewpoint planning to achieve the synchronous optimum of measurement efficiency and data integrity. Firstly, according to the measurable region of the surface structured light 3D measurement system and CAD model, the candidate viewpoint set is randomly generated in the sampling space, and the visibility matrix is constructed by analyzing whether each candidate viewpoint is visible to each patch of the model. Then, the surface reflection model of sheet metal parts with strong reflection is constructed, and the reflection coefficient of the visible patches under each viewpoint is calculated according to the reflection model. Based on this, the measurability of the visible patches of the viewpoint under multiple exposures is calculated, and the visibility matrix is updated. Lastly, through the viewpoint quality evaluation function constructed based on the data coverage increment and multiple-exposure time, the viewpoint with the highest quality is selected heuristically until the coverage requirement is met. Experiments show that the algorithm can improve the measurement efficiency and ensure the integrity of the measurement data.

Materials keeping thickness in atomic scale but extending primarily in lateral dimensions offer properties attractive for many emerging applications. However, compared to crystalline counterparts, synthesis of atomically thin films in the highly disordered amorphous form, which avoids nonuniformity and defects associated with grain boundaries, is challenging due to their metastable nature. Here we present a scalable and solution-based strategy to prepare large-area, freestanding quasi-2D amorphous carbon nanomembranes with predominant sp 2 bonding and thickness down to 1–2 atomic layers, from coal-derived carbon dots as precursors. These atomically thin amorphous carbon films are mechanically strong with modulus of 400 ± 100 GPa and demonstrate robust dielectric properties with high dielectric strength above 20 MV cm −1 and low leakage current density below 10 −4 A cm −2 through a scaled thickness of three-atomic layers. They can be implemented as solution-deposited ultrathin gate dielectrics in transistors or ion-transport media in memristors, enabling exceptional device performance and spatiotemporal uniformity.

Collagen is abundant but exposed in tumor due to the abnormal tumor blood vessels, thus is considered as a tumor-specific target. The A3 domain of von Willebrand factor (vWF A3) is a kind of collagen-binding domain (CBD) which could bind collagen specifically. Previously we reported a chemosynthetic CBD-SIRPαFc conjugate, which could block CD47 and derived tumor-targeting ability by CBD. CBD-SIRPαFc conjugate represented improved anti-tumor efficacy with increased MHC II+ M1 macrophages, but the uncertain coupling ratio remained a problem. Herein, we produced a vWF A3-SIRPαFc fusion protein through eukaryotic expression system. It was examined at both molecular and cellular levels with its collagen affinity, uninfluenced original affinity to targets and phagocytosis-promoting function compared to unmodified SIRPαFc. Living imaging showed that vWF A3-SIRPαFc fusion protein derived the improved accumulation and retention in tumor than SIRPαFc. In the MC38 allograft model, vWF A3-SIRPαFc demonstrated a superior tumor-suppressing effect, characterized by increased MHC II+ M1 macrophages and T cells (particularly CD4+ T cells). These results revealed that vWF A3-SIRPαFc fusion protein derived tumor-targeting ability, leading to improved anti-tumor immunotherapeutic efficacy compared to SIRPαFc. Altogether, vWF A3 improved the anti-tumor efficacy and immune-activating function of SIRPαFc, supporting targeting tumor collagen as a possible targeted strategy.

Recognizing and localizing queried objects in point clouds is a critical technique for robotic manipulation and bin picking tasks. Even though it has been steadily studied, it is still a challenging task for scenes with heavy occlusion and clutter. To copy with this intractable problem, this paper presents a fast and robust 3D object recognition framework, especially an efficient high compatibility correspondence grouping (HCCG) technique achieved by the correspondence ranking, clustering and expanding operations. Utilizing the HCCG technique, we first cluster the compatible correspondences into several high compatibility correspondence groups. Then, for each group, a 6DoF pose hypothesis is generated by using a point pair feature constraints (PPFCs)-based outlier removal module and a local reference frame (LRF)-based pose estimation algorithm. Finally, a robust pose verification operator is carried out to reject the false positive pose hypotheses and pick up the correct target object pose. Our approach is not only suitable for the multi-object recognition task, but also inherently yields the capability of detecting multiple instances thanks to the efficient HCCG technique. Extensive experiments on four challenging datasets show that our approach yields efficient and timely solutions and its advantages are further verified by comparing with the latest methods.

Three-dimensional feature description for a local surface is a core technology in 3D computer vision. Existing descriptors perform poorly in terms of distinctiveness and robustness owing to noise, mesh decimation, clutter, and occlusion in real scenes. In this paper, we propose a 3D local surface descriptor using point-pair transformation feature histograms (PPTFHs) to address these challenges. The generation process of the PPTFH descriptor consists of three steps. First, a simple but efficient strategy is introduced to partition the point-pair sets on the local surface into four subsets. Then, three feature histograms corresponding to each point-pair subset are generated by the point-pair transformation features, which are computed using the proposed Darboux frame. Finally, all the feature histograms of the four subsets are concatenated into a vector to generate the overall PPTFH descriptor. The performance of the PPTFH descriptor is evaluated on several popular benchmark datasets, and the results demonstrate that the PPTFH descriptor achieves superior performance in terms of descriptiveness and robustness compared with state-of-the-art algorithms. The benefits of the PPTFH descriptor for 3D surface matching are demonstrated by the results obtained from five benchmark datasets.

Structured light method is one of the best methods for automated 3D measurement in industrial production due to its stability and speed. However, when the surface of industrial parts has high dynamic range (HDR) areas, e.g. rust, oil stains, or shiny surfaces, phase calculation errors may happen due to low modulation and pixel over-saturation in the image, making it difficult to obtain accurate 3D data. This paper classifies and summarizes the existing high dynamic range structured light 3D measurement technologies, compares the advantages and analyzes the future development trends. The existing methods are classified into multiple measurement fusion (MMF) and single best measurement (SBM) based on the measurement principle. Then, the advantages of the various methods in the two categories are discussed in detail, and the applicable scenarios are analyzed. Finally, the development trend of high dynamic range 3D measurement based on structed light is proposed.

This report documents the outcomes of the Tri-Laboratory Materials Workshop that was held July 31 and August 1, 2019 to begin addressing the needs, opportunities, and challenges associated with the development, fabrication, and testing of the needed materials and components for integrated hybrid energy systems (i.e., incorporating nuclear, fossil, and renewables for electric and thermal applications). This was accomplished by assembling the research program leads and principal investigators at Idaho National Laboratory (INL), National Energy Technology Laboratory (NETL), and National Renewable Energy Laboratory (NREL), who support the research and development of new technology and system integration. The team then identified and prioritized key materials development needs. This effort was intended to enhance communications and synergy among the Tri-Lab partners. Advanced functional and structural materials are central to transformative energy technologies for energy generation, conversion, delivery, and storage. With that in mind, the workshop focused on identifying and assessing the foundational materials research needs at both the basic and applied levels. Materials challenges include the ability to withstand harsh environments, such as high temperatures and pressures, corrosion, oxidation, or irradiation while maintaining flexible mission profiles and long service lifespans. Advanced energy system material challenges and needs range from materials for the capture, upgrading/concentration, storage, and delivery of low-grade heat to materials for high temperature environments that involve liquid metals, molten salt, and very high temperature gas heat delivery and storage systems. Material improvements are needed for hybrid energy systems due to accelerated corrosion and stress-fatigue failure of materials and equipment, which results from increased frequency and amplitude of thermal, mechanical, and electrical cycling of systems components. Multifunctional materials are needed for high temperature solid-oxide fuel cells, advanced electrochemical reactors, and in-process separation. Relative to materials manufacturing, application of advanced additive and subtractive methods need to be understood to develop both thin-layer homogenous materials and materials of graded composition. Materials modeling and machine learning will be critical to accelerate the design and production of power electronics, and nuclear reactor materials and fuel, as well as to gain an understanding of beneficial materials phenomena or deleterious microstructure evolution. There is also a need for standardized models, computational structures, data reporting protocols and modeling tools across the three laboratories. This would allow consistent results, analysis, and data sharing. Combining computational capabilities between the three laboratories (e.g., hardware, software) would greatly increase computational capabilities and throughput. The workshop identified the need for laboratories to anticipate and address problems that will occur during scale-up. Laboratory work must connect with industry to ensure that research focuses on processes that are scalable and marketable. Industry input and perspective are essential to guide laboratory research to meet these requirements and deploy new technology in industrial demonstrations. Another aspect of scale-up is the integration of multiple systems since new challenges often arise at the subsystem interfaces. Establishing a scale-up manufacturing demonstration/pilot plant, potentially as an industrial user facility, would be beneficial to the laboratories and industry. That modular scale-up manufacturing demonstration/pilot plant would allow researchers to find and resolve interface problems that cannot be identified by focusing only on individual parts. Communication exchanges among the organizers, attendees, and workshop survey responses indicate that the workshop was successful in achieving its goal to identify key technology gaps and research needs. Strong positive feedback was received on the sharing of ideas, capabilities, talent, and passion to move forward on the materials-related action items.

Understanding the complicated crystallization process is important for controlling the performance reproducibility and stability in high temperature-selective laser sintering (HT-SLS). Therefore, in this paper, the performance of HT-SLS processed polyetheretherketone (PEEK) is analyzed from the perspective of crystallization kinetics. It is found that each layer of sintered powder undergoes the dynamic non-isothermal crystallization promoted by cold powder coating (CPC) and the quasi-static isothermal crystallization during the part bed temperature (T-b) maintenance of HT-SLS. The kinetic analysis shows 321 degrees C is a theoretical T-b for PEEK. However, due to the great sensitivity to the temperature drop of CPC, the practical T-b is set around 332 degrees C, at which the 1/t(1/2) approaches zero according to the extrapolated analysis of Hoffman-Lauritzen theory. The shrinkage is only caused by the dynamic non-isothermal crystallization of CPC, and the minimum powder feeding temperature is found to be 250 degrees C by Nakamura analysis. In addition, the quasi-static maintaining of T-b during HT-SLS can lead to extended planar subunit along c-axis, increased crystallite size perpendicular to (1 1 0) and (2 0 0), and higher mechanical strength.

The ram speed of a steam hammer is an important parameter that directly affects the forming performance of forgers. This parameter must be monitored regularly in practical applications in industry. Because of the complex and dangerous industrial environment of forging equipment, non-contact measurement methods, such as stereo vision, might be optimal. However, in actual application, the field of view (FOV) required to measure the steam hammer is extremely large, with a value of 2⁻3 m, and heavy steam hammer, at high-speed, usually causes a strong vibration. These two factors combine to sacrifice the accuracy of measurements, and can even cause the failure of measurements. To solve these issues, a bundle-adjustment-principle-based system calibration method is proposed to realize high-accuracy calibration for a large FOV, which can obtain accurate calibration results when the calibration target is not precisely manufactured. To decrease the influence of strong vibration, a stationary world coordinate system was built, and the external parameters were recalibrated during the entire measurement process. The accuracy and effectiveness of the proposed technique were verified by an experiment to measure the ram speed of a counterblow steam hammer in a die forging device.