Recent Progress in Two-Dimensional Materials for Electrocatalytic CO2 Reduction

Electrocatalytic CO2 reduction (ECR) is an attractive approach to convert atmospheric CO2 to value-added chemicals and fuels. However, this process is still hindered by sluggish CO2 reaction kinetics and the lack of efficient electrocatalysts. Therefore, new strategies for electrocatalyst design should be developed to solve these problems. Two-dimensional (2D) materials possess great potential in ECR because of their unique electronic and structural properties, excellent electrical conductivity, high atomic utilization and high specific surface area. In this review, we summarize the recent progress on 2D electrocatalysts applied in ECR. We first give a brief description of ECR fundamentals and then discuss in detail the development of different types of 2D electrocatalysts for ECR, including metal, graphene-based materials, transition metal dichalcogenides (TMDs), metal–organic frameworks (MOFs), metal oxide nanosheets and 2D materials incorporated with single atoms as single-atom catalysts (SACs). Metals, such as Ag, Cu, Au, Pt and Pd, graphene-based materials, metal-doped nitric carbide, TMDs and MOFs can mostly only produce CO with a Faradic efficiencies (FE) of 80~90%. Particularly, SACs can exhibit FEs of CO higher than 90%. Metal oxides and graphene-based materials can produce HCOOH, but the FEs are generally lower than that of CO. Only Cu-based materials can produce high carbon products such as C2H4 but they have low product selectivity. It was proposed that the design and synthesis of novel 2D materials for ECR should be based on thorough understanding of the reaction mechanism through combined theoretical prediction with experimental study, especially in situ characterization techniques. The gap between laboratory synthesis and large-scale production of 2D materials also needs to be closed for commercial applications.publishedVersio

VSQL: Variational Shadow Quantum Learning for Classification

Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.Comment: 20 pages. To appear in the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021

Variational Quantum Singular Value Decomposition

Singular value decomposition is central to many problems in engineering and scientific fields. Several quantum algorithms have been proposed to determine the singular values and their associated singular vectors of a given matrix. Although these algorithms are promising, the required quantum subroutines and resources are too costly on near-term quantum devices. In this work, we propose a variational quantum algorithm for singular value decomposition (VQSVD). By exploiting the variational principles for singular values and the Ky Fan Theorem, we design a novel loss function such that two quantum neural networks (or parameterized quantum circuits) could be trained to learn the singular vectors and output the corresponding singular values. Furthermore, we conduct numerical simulations of VQSVD for random matrices as well as its applications in image compression of handwritten digits. Finally, we discuss the applications of our algorithm in recommendation systems and polar decomposition. Our work explores new avenues for quantum information processing beyond the conventional protocols that only works for Hermitian data, and reveals the capability of matrix decomposition on near-term quantum devices.Comment: 23 pages, v3 accepted by Quantu

Solvent-free lithium iron phosphate cathode fabrication with fibrillation of polytetrafluoroethylene

Fabricating electrode for lithium-ion batteries (LiBs) with solvent-free (SF) procedure can save energy and improve electrochemical performance simultaneously. Polymer fibrillation is one of the most promising SF procedures due to its feasibility for upscale production. The hardness of lithium iron phosphate (LFP) impedes its SF fabrication with polytetrafluoroethylene (PTFE) fibrillation. In this study, we successfully expanded PTFE fibrillation for SF LFP electrode fabrication with the help of carbon nanotubes (CNTs). CNTs increase the conductivity of electrode, and act as matrix for LFP particles to ensure relative displacement to further fibrillate PTFE to form self-supporting electrode film when the dry mixture was hot rolled. The SF LFP/hard carbon full cells were fabricated and demonstrated comparable electrochemical performance to slurry casting (SC) fabricated LFP electrode. The initial coulombic efficiency (ICE) of full cell increased to more than 95% after prelithiation.publishedVersio

Leveraging Synergies by Combining Polytetrafluorethylene with Polyvinylidene Fluoride for Solvent-Free Graphite Anode Fabrication

Solvent-free graphite anode is fabricated successfully with the synergistic effect of polytetrafluorethylene (PTFE) and polyvinylidene fluoride (PVDF). PTFE acts as a processing aid reagent to form a self-supporting electrode film, while PVDF acts as a functional binder when PTFE decomposes in the first lithiation process. The solvent-free graphite electrode with high loading of 15 mg cm−2 shows good stability with more than 95% capacity retention after 50 charge/discharge cycles under the current of 0.23 mA cm−2. Electrodes with extra high loading of 27 mg cm−2 (8.2 mAh cm−2) are fabricated and show good stability. Initial coulombic efficiency increases to 89% after prelithiation in the full cell with lithium iron phosphate as cathode. The capacity retention of full cells is more than 80% after 110 cycles under the current of 0.7 mA cm−2 in coin cells. The roll-to-roll production makes the procedure compatible with current commercial lithium-ion batteries production lines, exhibiting great potential for upscaling production.publishedVersio

Theoretical study of single transition metal atom catalysts supported on two-dimensional Nb2NO2 for efficient electrochemical CO2 reduction to CH4

The design of highly efficient catalysts for electrochemical reduction CO2 (ECR) to value-add chemicals and fuels is important for CO2 conversion technologies. In this work, earth abundant transition metal (TM = V, Cr, Mn, Fe, Co and Ni) atoms embedded into two-dimensional (2D) Nb2NO2 (TM@Nb2NO2) as single-atom catalysts (SACs) for ECR was investigated by first-principles study. We demonstrated that Nb2NO2 can be an excellent substrate for anchoring single TM atom due to its excellent stability and electronic conductivity. Besides, V, Cr and Ni@Nb2NO2 could effectively promote CO2 adsorption and reduction. All TM@Nb2NO2 exhibit high selectivity towards CH4, and V, Cr and Ni@Nb2NO2 show low limiting potentials. The activity origin was revealed by analysing adsorption energy, d band centre, bonding/antibonding population and the change of valence state of TM atoms.publishedVersio

Transition metal single-atom supported on PC3 monolayer for highly efficient hydrogen evolution reaction by combined density functional theory and machine learning study

It is essential to develop non-precious metal-based alternatives used in hydrogen evolution reaction (HER) due to high cost and scarcity of Pt-based catalysts. Herein, through density functional theory (DFT) calculations, the HER activity over 26 single-atom anchored phosphorus carbide (PC3) monolayer (TM@PC3) has been systematically investigated. Results indicate that ΔG*H of V, Fe, Nb, Mo, and Pd@PC3 are lower than that of Pt (1 1 1) catalyst, with 0.03, −0.03, −0.07, −0.04, and − 0.02 eV, respectively. By imposing the criterion window (−0.2 ≤ ΔG*H ≤ 0.2 eV), the d band centre (εd) for catalysts with excellent HER ability is in the range of − 0.68–0.41 eV. Besides, the five promising HER catalysts follow Volmer-Tafel mechanism. Fe, Nb, and Mo@PC3 show activation barriers of 0.75, 0.74, and 0.55 eV, lower than that of Pt. Machine learning (ML) was employed to explore the intrinsic relationship between catalytic performance and feature parameters. We demonstrated that the first ionization energy, bond length of TM − H and d band center are more correlated with hydrogen adsorption behaviour. Our work not only predicts that Fe, Nb, and Mo@PC3 can be substitutes for Pt metal in HER, but also reveals that the intrinsic correlation between catalytic activity and feature parameters by combining DFT and ML investigations.publishedVersio

Revisiting Polytetrafluorethylene Binder for Solvent-Free Lithium-Ion Battery Anode Fabrication

Solvent-free (SF) anodes with different carbon materials (graphite, hard carbon, and soft carbon) were fabricated to investigate the stability of different anodes with polytetrafluorethylene (PTFE) degradation. The graphite anode with large volume variation during the charge/discharge process showed poor cycle life performance, while hard carbon and soft carbon with low-volume expansion showed good cycle life. The SF hard carbon electrodes with a high loading of 10.7 mg/cm2 revealed good long-term cycling performance similar to conventional slurry-casting (CSC) electrodes. It demonstrated nearly 90% capacity retention after 120 cycles under a current of 1/3 C with LiNi0.5Co0.2Mn0.3O2 (NCM523) as cathode in coin cell. The rate capability of the high-loading SF electrodes also is comparable to the CSC electrodes. The high stability of SF hard carbon and soft carbon anodes was attributed to its low-volume variation, which could maintain their integrity even though PTFE was defluorinated to amorphous carbon irreversibly. However, the reduced amorphous carbon cannot tolerate huge volume variation of graphite during cycling, resulting in poor stability.publishedVersio

Chidamide and Decitabine in Combination with a HAG Priming Regimen for Acute Myeloid Leukemia with TP53 Mutation

We analyzed the treatment effects of chidamide and decitabine in combination with a HAG (homoharringtonine, cytarabine, G-CSF) priming regimen (CDHAG) in acute myeloid leukemia (AML) patients with TP53 mutation. Seven TP53 mutated AML patients were treated with CDHAG. The treatment effects were assessed using hemogram detection and bone marrow aspirate. The possible side effects were evaluated based on both hematological and non-hematological toxicity. Four of the seven patients were classified as having achieved complete remission after CDHAG treatment; one patient was considered to have achieved partial remission, and the remaining two patients were considered in non-remission. The overall response rate (ORR) to CDHAG was 71.4%. Regarding the side effects, the hematological toxicity level of the seven patients ranged from level III to level IV, and infections that occurred at lung, blood, and skin were recorded. Nausea, vomiting, liver injury, and kidney injury were also detected. However, all side effects were attenuated by proper management. The CDHAG regimen clearly improved the ORR (71.4%) of TP53-mutated AML patients, with no severe side effects

SpacePulse: Combining Parameterized Pulses and Contextual Subspace for More Practical VQE

In this paper, we explore the integration of parameterized quantum pulses with the contextual subspace method. The advent of parameterized quantum pulses marks a transition from traditional quantum gates to a more flexible and efficient approach to quantum computing. Working with pulses allows us to potentially access areas of the Hilbert space that are inaccessible with a CNOT-based circuit decomposition. Compared to solving the complete Hamiltonian via the traditional Variational Quantum Eigensolver (VQE), the computation of the contextual correction generally requires fewer qubits and measurements, thus improving computational efficiency. Plus a Pauli grouping strategy, our framework, SpacePulse, can minimize the quantum resource cost for the VQE and enhance the potential for processing larger molecular structures