作者：
Zhang, YoukuiPu, YujuanLi, WenhaoLin, YunxiangLi, HaoyuanWu, YingshuoDuan, TaoSouthwest Univ Sci & Technol
Sch Nucl Sci & Technol State Key Lab Environm Friendly Energy Mat Mianyang 621010 Sichuan Peoples R China Anhui Univ
Inst Phys Sci Leibniz Int Joint Res Ctr Mat Sci Anhui Prov Ctr Free Electron Laser & High Magnet Field Hefei 230601 Peoples R China Anhui Univ
Inst Informat Technol Leibniz Int Joint Res Ctr Mat Sci Anhui Prov Ctr Free Electron Laser & High Magnet Field Hefei 230601 Peoples R China Chongqing Univ
Coll Chem & Chem Engn Chongqing 401331 Peoples R China
Rationally optimizing the atomic and electronic structure of electrocatalysts is an effective strategy to improve the activity of the electrocatalytic oxygen evolution reaction (OER), yet it remains challenging. In th...
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Rationally optimizing the atomic and electronic structure of electrocatalysts is an effective strategy to improve the activity of the electrocatalytic oxygen evolution reaction (OER), yet it remains challenging. In this work, atomic heterointerface engineering is developed to accelerate OER by decorating iridium atoms on low-crystalline cobalt hydroxide nanosheets (Ir-Co(OH) x ) via oxygen-coordinated bonds to modulate the local electronic structure. Leveraging detailed spectroscopic characterizations, the Ir species were proved to promote charge transfer through Ir-O-Co coordination between the Ir atom and the Co(OH) x support. As a result, the optimized Ir-Co(OH) x exhibits excellent electrocatalytic OER activity with a low overpotential of 251 mV to drive 10 mA cm-2, which is 63 mV lower than that of pristine Co(OH) x . The experimental results and density functional theory calculations reveal that the isolated Ir atoms can regulate the local coordination environment and electronic configuration of Co(OH) x , thus accelerating the catalytic OER kinetics. This work provides an atomistic strategy for the electronic modulation of metal active sites in the design of high-performance electrocatalysts.
Previous works on the integration of ranging and communication were implemented at the functional layer, requiring substantial additional resources when implementing range estimation algorithms, which imposed challeng...
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Previous works on the integration of ranging and communication were implemented at the functional layer, requiring substantial additional resources when implementing range estimation algorithms, which imposed challenges for practical implementation. To reduce the complexity and resource consumption, this article presents a hardware-level integration approach using signal processing techniques from the frequency-modulated continuous wave (FMCW) radar inside frequency double duplexing (FDD) communication platforms. By applying the baseband sampling on the chirp wave, the generation and mixture process of the FMCW radar is transferred to the baseband in the proposed baseband direct sample (BBDS) structure, where the range estimation is performed by the round trip of the chirp between the main equipment (ME) and the secondary equipment (SE). Analysis of the systematic error suggests that with the combination of up-chirp and down-chirp waveform, the impact of constant frequency offset (FO) and timing offset of the oscillators in ME and SE on the ranging error can be significantly mitigated. To enhance the range estimation under the restricted bandwidth, a phase-difference-based single-tone estimation (PdB-STE) algorithm is performed under the scheme of frequency estimation for single-tone signals. The initial simulation compares the root mean square error (RMSE) of the FMCW radar with linear chirp and the proposed BBDS structure with combined chirp under an additive white Gaussian noise (AWGN) channel, and the results demonstrate that the BBDS achieves a lower RMSE. Afterward, simulations of the BBDS structure are performed under the AWGN and the Rician fading channels. An RMSE of 0.1 m in the AWGN channel with a maximum Doppler frequency shift of 100 Hz, and that of 45 m is obtained in the Rician channel with the extended vehicle A (EVA) channel model. Hardware verification utilizing an AD9361 transceiver attains an RMSE below 0.8 m. This result complies with the prior
The quality of seeds is crucial for influence maximization in social networks. Seed selection algorithms based on deep reinforcement learning (DRL) combine the representation capabilities of deep learning with the dec...
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The quality of seeds is crucial for influence maximization in social networks. Seed selection algorithms based on deep reinforcement learning (DRL) combine the representation capabilities of deep learning with the decision-making abilities of reinforcement learning, showing great potential in solving the influence maximization problem. In this paper, an end-to-end trained dual coupled graph neural network (DGN) for seed selection is proposed. This method not only ingeniously utilizes dual coupled graph neural networks for node embedding but also employs a deep Q-network (DQN) from deep reinforcement learning to explore the topology and node attribute information of social networks to generate context-rich node vector representations. Moreover, it can train the network to approximate the Q-function through DQN without prior knowledge, allowing it to adaptively select the optimal strategy based on the current state and action, thereby maximizing the influence spread of the selected seed nodes. Extensive experiments on synthetic and real networks demonstrate that the proposed DGN not only achieves performance very close to or even surpassing current state-of-the-art models like ToupleGDD but also exhibits better robustness, making it more suitable for the influence maximization problem in today's large-scale, complex, and dense social networks.
The CRISPR-Cas system, particularly CRISPR-Cas12a and CRISPR-Cas13a, has been widely utilized in constructing various biosensors due to their "trans-cleavage" ability as a means of signal amplification. Howe...
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The CRISPR-Cas system, particularly CRISPR-Cas12a and CRISPR-Cas13a, has been widely utilized in constructing various biosensors due to their "trans-cleavage" ability as a means of signal amplification. However, this universal "trans-cleavage" characteristic also presents a challenge for realizing CRISPR-Cas multiplexed bioanalysis. Besides, potential signal cascading interference and complicated design are notable obstacles in CRISPR-Cas multiplexed bioanalysis. Herein, we propose a mass spectrometry method that leverages the CRISPR-Cas12a/13a system to achieve simultaneous detection of ctDNA and miRNA. Based on the properties of the CRISPR-Cas12a/13a system, two types of nanoparticle reporter probes have been engineered, using cancer-related biomarkers ctDNA and miR-21 as our model analytes. The nanoparticle tags, which intrinsically incorporated millions of detectable atoms, combined with the CRISPR-Cas12a/Cas13a system's "trans-cleavage" ability, allow the proposed mass spectrometry strategy to achieve fmol-level detection limits without any nucleic acid amplification procedures. The assay was successfully applied to human serum samples, demonstrating its potential for early disease diagnosis and progression tracking.
Establishing an optimal configuration for the electron transport layer (ETL) and a compliant perovskite interface is pivotal in advancing the creation of high-performance, hysteresis-free, and resilient perovskite sol...
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Establishing an optimal configuration for the electron transport layer (ETL) and a compliant perovskite interface is pivotal in advancing the creation of high-performance, hysteresis-free, and resilient perovskite solar cells (PSCs). Amongst various strategies, interface engineering emerges as a highly feasible and potent means to alleviate interfacial non-radiative recombinations, issues typically rooted in defects, tensile stresses, and energy level discrepancies at the interface. Our investigation solidifies the efficacy of incorporating Imidazolium Salt (NOI:1N-3-acetic acid-imidazole) within the SnO2/perovskite interface as a strategic intervention for remodeling this vital frontier. The integration of NOI fosters a synergistic interface, seamlessly bridging the perovskite with the SnO2 ETL, effectively mitigating tensile strains and passivating underlying interface defects. Implementation of the NOI-based treatment regimen has notably propelled device performance, evidenced by a PCE escalation from 21.5% to 23.3%, coupled with a marked increase in open-circuit voltage (VOC) from 1.15 V to 1.18 V. Consequently, this methodology presents a concise yet powerful pathway for augmenting PSCs' operational excellence.
A palladium-catalyzed decarboxylative allylic sulfonylation reaction of vinyloxazolidine-2,4-diones with inexpensive and readily available sodium sulfinates as sulfonylation reagents has been developed. Under the cata...
A palladium-catalyzed decarboxylative allylic sulfonylation reaction of vinyloxazolidine-2,4-diones with inexpensive and readily available sodium sulfinates as sulfonylation reagents has been developed. Under the catalysis of Pd(PPh3)4, a wide range of gamma-sulfonyl-alpha,beta-unsaturated amides can be synthesized in good to excellent yields. The developed protocol is characterized by exclusive regioselectivity, mild reaction conditions, broad substrate scope, good functional group tolerance, and suitable for gram-scale synthesis.
Voltage sags are one of the primary factors in power quality issues that lead to losses for sensitive users and reduce the operation resilience of distribution networks. However, due to the lack of accessibility in se...
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Voltage sags are one of the primary factors in power quality issues that lead to losses for sensitive users and reduce the operation resilience of distribution networks. However, due to the lack of accessibility in sensitive users' production information, accurately quantifying the resilience of distribution networks under the impact of voltage sags is challenging. In this letter, first an operation resilience index using a trapezoidal curve is defined. Considering the varying tolerance levels of sensitive users to voltage sags, a feature indices system is established using the adaptive S-transform, and a sample dataset is generated through the Monte Carlo method. Finally, a mapping relationship between sag characteristics and operation resilience indices is established using the XGBoost-stacking algorithm. Simulations based on voltage sag recorded data validate the effectiveness and practicality of the proposed method. This data-physics hybrid-driven model offers a quantitative approach for developing resilience enhancement strategies.
In n-i-p perovskite solar cells (PSCs) with tin oxide (SnO2) as the electron transport layer, the SnO2/perovskite interface serves dual functions: facilitating charge extraction and promoting perovskite crystal growth...
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In n-i-p perovskite solar cells (PSCs) with tin oxide (SnO2) as the electron transport layer, the SnO2/perovskite interface serves dual functions: facilitating charge extraction and promoting perovskite crystal growth. However, significant nonradiative recombination, energy misalignment, and suboptimal interfacial bonding at the SnO2/perovskite interface result in efficiency loss and compromised stability. In this work, sulfonyl diimidazole (SDI) was introduced as a surface modifier for SnO2. SDI acts as a dual-modifier at the buried interface, binding strongly to both the SnO2 and perovskite layers. SDI effectively passivates SnO2 surface defects, optimizes band arrangement for better interfacial contact, and facilitates perovskite growth. This effectively promotes charge carrier transport, inhibits charge carrier recombination, and alleviates the film strain. The SDI-incorporated device demonstrated an improved power conversion efficiency (PCE) of 23.31%, surpassing the control device's efficiency of 21.61%, alongside an elevation in fill factor from 77.3% to 81.56%. The optimized device demonstrates a 90% retention of its initial PCE after enduring 1000 h of aging, significantly outlasting the pristine SnO2-based counterpart. Consequently, the modification of interfaces utilizing SDI represents a viable approach to enhance the interface characteristics, elevate the quality of crystallization, and achieve high-performance PSCs.
Chiral phosphoric acid-catalyzed enantioselective Friedel-Crafts alkylation of 4-aminoindoles with beta,gamma-alkynyl-alpha-ketoimines has been developed. A range of optically pure C7-functionalized indoles bearing a ...
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Chiral phosphoric acid-catalyzed enantioselective Friedel-Crafts alkylation of 4-aminoindoles with beta,gamma-alkynyl-alpha-ketoimines has been developed. A range of optically pure C7-functionalized indoles bearing a quaternary alpha-amino acid or trifluoromethylated tetrasubstituted alkylamine motif were obtained with up to 98% yield and 99% ee. This protocol effectively incorporates site-specific Friedel-Crafts alkylation at the C7 site of 4-aminoindoles and regio-specific quaternary stereocenter construction at the alpha-position of beta,gamma-alkynyl-alpha-ketoimines, and opens a new avenue to access chiral C7-functionalized indoles.
Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis...
Recent studies have provided promising evidence that neuroimaging data can predict treatment outcomes for patients with major depressive disorder (MDD). As most of these studies had small sample sizes, a meta-analysis is warranted to identify the most robust findings and imaging modalities, and to compare predictive outcomes obtained in magnetic resonance imaging (MRI) and studies using clinical and demographic features. We conducted a literature search from database inception to July 22, 2023, to identify studies using pretreatment clinical or brain MRI features to predict treatment outcomes in patients with MDD. Two meta-analyses were conducted on clinical and MRI studies, respectively. The meta-regression was employed to explore the effects of covariates and compare the predictive performance between clinical and MRI groups, as well as across MRI modalities and intervention subgroups. Meta-analysis of 13 clinical studies yielded an area under the curve (AUC) of 0.73, while in 44 MRI studies, the AUC was 0.89. MRI studies showed a higher sensitivity than clinical studies (0.78 vs. 0.62, Z = 3.42, P = 0.001). In MRI studies, resting-state functional MRI (rsfMRI) exhibited a higher specificity than task-based fMRI (tbfMRI) (0.79 vs. 0.69, Z = -2.86, P = 0.004). No significant differences in predictive performance were found between structural and functional MRI, nor between different interventions. Of note, predictive MRI features for treatment outcomes in studies using antidepressants were predominantly located in the limbic and default mode networks, while studies of electroconvulsive therapy (ECT) were restricted mainly to the limbic network. Our findings suggest a promise for pretreatment brain MRI features to predict MDD treatment outcomes, outperforming clinical features. While tasks in tbfMRI studies differed, those studies overall had less predictive utility than rsfMRI data. Overlapping but distinct network-level measures predicted antidepressants and ECT o
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