10 research outputs found
Robotic Removal and Collection of Screws in Collaborative Disassembly of End-of-Life Electric Vehicle Batteries
No full textThe recycling and remanufacturing of end-of-life (EoL) electric vehicle (EV) batteries are urgent challenges for a circular economy. Disassembly is crucial for handling EoL EV batteries due to their inherent uncertainties and instability. The human–robot collaborative disassembly of EV batteries as a semi-automated approach has been investigated and implemented to increase flexibility and productivity. Unscrewing is one of the primary operations in EV battery disassembly. This paper presents a new method for the robotic unfastening and collecting of screws, increasing disassembly efficiency and freeing human operators from dangerous, tedious, and repetitive work. The design inspiration for this method originated from how human operators unfasten and grasp screws when disassembling objects with an electric tool, along with the fusion of multimodal perception, such as vision and touch. A robotic disassembly system for screws is introduced, which involves a collaborative robot, an electric spindle, a screw collection device, a 3D camera, a six-axis force/torque sensor, and other components. The process of robotic unfastening and collecting screws is proposed by using position and force control. Experiments were carried out to validate the proposed method. The results demonstrate that the screws in EV batteries can be automatically identified, located, unfastened, and removed, indicating potential for the proposed method in the disassembly of EoL EV batteries
Inner Oracles: Input-Specific Assertions on Internal States
No full textTraditional test oracles are defined on the outputs of test executions, and cannot assert internal states of executions. Traditional assertions are common to all test execution, and are usually more difficult to construct than on oracle for one test input. In this paper we propose the concept of inner oracles, which are assertions on internal states that are specific to one test input. We first motivate the necessity of inner oracles, and then show that it can be implemented easily using the available programming mechanisms. Next, we report two initial empirical studies on inner oracles, showing that inner oracles have a significant impact on both the fault-detection capability of tests and the performance of test suite reduction. Finally, we highlight the implications of inner oracles on several research and practical problems.EICPCI-S(ISTP)[email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Task Allocation and Sequence Planning for Human–Robot Collaborative Disassembly of End-of-Life Products Using the Bees Algorithm
No full textRemanufacturing, which benefits the environment and saves resources, is attracting increasing attention. Disassembly is arguably the most critical step in the remanufacturing of end-of-life (EoL) products. Human–robot collaborative disassembly as a flexible semi-automated approach can increase productivity and relieve people of tedious, laborious, and sometimes hazardous jobs. Task allocation in human–robot collaborative disassembly involves methodically assigning disassembly tasks to human operators or robots. However, the schemes for task allocation in recent studies have not been sufficiently refined and the issue of component placement after disassembly has not been fully addressed in recent studies. This paper presents a method of task allocation and sequence planning for human–robot collaborative disassembly of EoL products. The adopted criteria for human–robot disassembly task allocation are introduced. The disassembly of each component includes dismantling and placing. The performance of a disassembly plan is evaluated according to the time, cost, and utility value. A discrete Bees Algorithm using genetic operators is employed to optimise the generated human–robot collaborative disassembly solutions. The proposed task allocation and sequence planning method is validated in two case studies involving an electric motor and a power battery from an EoL vehicle. The results demonstrate the feasibility of the proposed method for planning and optimising human–robot collaborative disassembly solutions
Robotic disassembly of snap-fit plug connectors in end-of-life electric vehicle batteries
No full textDisassembly is the first step in the remanufacturing of End-of-Life (EoL) Electric Vehicle (EV) batteries. Currently, many disassembly procedures for EV batteries are performed by human operators. Robotic disassembly of EV batteries is essential for increasing the efficiency of the process. A common operation in EV battery disassembly is removing plug connectors. This paper introduces a new method for automating the disassembly of a snap-fit plug connector using a specially designed tool. A control strategy combining force and position was implemented. Experiments were performed on a single connector to investigate and validate the proposed method for disassembling snap-fit plug connectors. The results show that the success rate and integrity rate of the method were both 100 % across 100 tests. Finally, the paper presents a case study on disassembling snap-fit plug connectors in an EV battery. The case study shows that the disassembly approach is feasible and practical, and it can facilitate the automated disassembly of EV batteries
Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity
No full textEffect of Hollow Structures on <i>T</i><sub>1</sub> and <i>T</i><sub>2</sub> Relaxivities and Their Application in Accurate Tumor Imaging
No full textGreat progress in precisely controlling the structures
of magnetic
nanoparticles has been made to investigate structure–relaxivity
relationships in recent years. However, the investigation of the influence
of hollow structures with unique interior structures on the relaxation
rate of magnetic nanoparticles is rare. Herein, we obtained a series
of hollow manganese-doped iron oxide nanoparticles (MnIONs) with different
void and dopant ratios through a controllable etch process and systemically
investigated the influence of hollow structures on the T1/T2 relaxation rate. Due
to the increased surface-to-volume (S/V) ratio, hollow MnIO nanoparticles
(HMNs) show increased T1 relaxivity compared
to solid MnIONs. The T1 relaxivities of
HMNs with different void ratios are proportion to the number of exposed
magnetic ions and electronic relaxation time value, which are determined
by the S/V ratio and dopant level. More importantly, HMNs exhibit
reduced saturated magnetization values with increased T2 relaxivities compared to solid MnIONs. The elevated T2 relaxivities of HMNs are attributed to the
increased number of magnetic cores per unit volume and magnetic field
inhomogeneity induced by hollow structures. These parameters are highly
dependent on their void ratios, thus eventually determining their T2 relaxivities. In vivo studies demonstrate
that HMNs with relatively high T1 or T2 relaxivity show superior sensitivity in tumor
detection to traditional T1 or T2 contrast agents. This work summarized the
effects and mechanisms of hollow structures on the T1 and T2 relaxation rates
of magnetic nanoparticles, providing examples in vivo for the design
of excellent T1 or T2 contrast agents (CAs) for early cancer diagnosis
Magnetic Field-Optimized Paramagnetic Nanoprobe for <i>T</i><sub>2</sub>/<i>T</i><sub>1</sub> Switchable Histopathological-Level MRI
No full textTraditional magnetic resonance imaging (MRI) contrast
agents (CAs)
are a type of “always on” system that accelerates proton
relaxation regardless of their enrichment region. This “always
on” feature leads to a decrease in signal differences between
lesions and normal tissues, hampering their applications in accurate
and early diagnosis. Herein, we report a strategy to fabricate glutathione
(GSH)-responsive one-dimensional (1-D) manganese oxide nanoparticles
(MONPs) with improved T2 relaxivities
and achieve effective T2/T1 switchable MRI imaging of tumors. Compared to traditional
contrast agents with high saturation magnetization to enhance T2 relaxivities, 1-D MONPs with weak Ms effectively increase the inhomogeneity of
the local magnetic field and exhibit obvious T2 contrast. The inhomogeneity of the local magnetic field of
1-D MONPs is highly dependent on their number of primary particles
and surface roughness according to Landau–Lifshitz–Gilbert
simulations and thus eventually determines their T2 relaxivities. Furthermore, the GSH responsiveness ensures
1-D MONPs with sensitive switching from the T2 to T1 mode in vitro and subcutaneous tumors to clearly delineate the boundary of glioma
and metastasis margins, achieving precise histopathological-level
MRI. This study provides a strategy to improve T2 relaxivity of magnetic nanoparticles and construct switchable
MRI CAs, offering high tumor-to-normal tissue contrast signal for
early and accurate diagnosis
Multivalence Manganese Mediated Dual ROS Generator for Augmented Chemodynamic Therapy and Activated Antitumor Immunogenic Responses
No full textThe efficacy of chemodynamic therapy (CDT) and its potential
to
induce immunogenic cell death (ICD) are highly dependent on the types
and efficiency of generated reactive oxygen species (ROS). Herein,
we innovatively develop a manganese platinum (MnPt) nanozyme with
dual ROS generation for magnetic resonance imaging guided CDT and
ICD. The multivalence state of Mn ions authorizes MnPt to simultaneously
generate hydroxyl radical and singlet oxygen under high H2O2 and acid condition, which obviously increases the types
of ROS. Significantly, the mild photothermal conversion and enzyme-like
activity endow MnPt to regulate tumor microenvironment and elevate
the operating rate, further increasing the ROS generation efficiency
and CDT efficacy in vitro/vivo. Moreover, the exaltation
on both types and generation efficiency of ROS significantly enhances
immunogenicity and initiates the immune response to limit tumor metastases.
This study provides a highly practical strategy for the accurate diagnosis
and efficient therapy of tumors
Self-repairing interphase reconstructed in each cycle for highly reversible aqueous zinc batteries
No full textAqueous zinc (Zn) chemistry features intrinsic safety, but suffers from severe irreversibility, as exemplified by low Coulombic efficiency, sustained water consumption and dendrite growth, which hampers practical applications of rechargeable Zn batteries. Herein, we report a highly reversible aqueous Zn battery in which the graphitic carbon nitride quantum dots additive serves as fast colloid ion carriers and assists the construction of a dynamic & self-repairing protective interphase. This real-time assembled interphase enables an ion-sieving effect and is found actively regenerate in each battery cycle, in effect endowing the system with single Zn2+ conduction and constant conformal integrality, executing timely adaption of Zn deposition, thus retaining sustainable long-term protective effect. In consequence, dendrite-free Zn plating/stripping at ~99.6% Coulombic efficiency for 200 cycles, steady charge-discharge for 1200 h, and impressive cyclability (61.2% retention for 500 cycles in a Zn | |MnO2 full battery, 73.2% retention for 500 cycles in a Zn | |V2O5 full battery and 93.5% retention for 3000 cycles in a Zn | |VOPO4 full battery) are achieved, which defines a general pathway to challenge Lithium in all low-cost, large-scale applications
Recommended from our members
Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity
No full textBackground: Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures. Results: Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine. Conclusions: The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism
