Lithium bonds are analogous to hydrogen bonds and are therefore expected to exhibit similar characteristics and functions. Additionally, the metallic nature and large atomic radius of Li bestow the ...Li bond with special features. As one of the most important applications of the element, Li batteries afford emerging opportunities for the exploration of Li bond chemistry. Herein, the historical development and concept of the Li bond are reviewed, in addition to the application of Li bonds in Li batteries. In this way, a comprehensive understanding of the Li bond in Li batteries and an outlook on its future developments is presented.
Lithium bonds that are present in lithium batteries are discussed in this Viewpoint, including historical developments, comparisons with hydrogen bonds, and their potential applications. Discourse on the chemistry of the Li bond can provide fruitful insight into the fundamental interactions within Li batteries and thus deliver a deeper understanding of their working mechanism.
This mixed-methods study examined the effectiveness of Rosetta Stone, a mobile-based language learning application, on Chinese EFL students’ listening, speaking, foreign language enjoyment (FLE), and ...foreign language anxiety (FLA). Two groups of intermediate Chinese EFL students from a language institute, with 33 students in the control group and 36 in the experimental group, were recruited as the participants. The treatment lasted for 3 months, with the experimental group using Rosetta Stone in addition to in-class instruction whereas the control group received only the regular in-class instruction. Data collection involved administering IELTS module tests of speaking and listening, FLE and FLA scales, as well as semi-structured interviews. ANCOVA, paired samples test, and content analysis were used for the data analysis of the quantitative and qualitative data. The results indicate that the experimental group showed significant improvements in their listening, speaking, FLE, and reduced FLA more than the control group. The results of the qualitative data indicated that the students had positive attitudes toward mobile-based language learning. The qualitative findings provided further support to the quantitative results and highlighted the convenience, flexibility, and engaging nature of the application. The outcomes stressed the potential benefits of using mobile-based language learning tools like Rosetta Stone as an effective supplementary method for improving EFL learning outcomes and promoting positive affective variables.
Plain language summary
This study looked at how a mobile language learning app called Rosetta Stone affected Chinese students’ listening, speaking, enjoyment of learning a foreign language, and anxiety about learning a foreign language. The researchers had two groups of intermediate Chinese students: one group used Rosetta Stone along with regular classroom instruction, and the other group only had regular classroom instruction. The study lasted for 3 months. The researchers tested the students’ listening and speaking skills, as well as their levels of enjoyment and anxiety in learning a foreign language. They also conducted interviews with the students. The results showed that the group using Rosetta Stone had significant improvements in listening, speaking, enjoyment, and reduced anxiety compared to the group that only had classroom instruction. The interviews supported these findings and showed that the students liked using the mobile app because it was convenient, flexible, and engaging. Overall, this study suggests that using mobile language learning apps like Rosetta Stone can be a helpful addition to regular classroom instruction, improving language learning outcomes and students’ positive attitudes. However, it’s important to note that this study had some limitations, such as the small sample size and focus on a specific language app, so further research is needed to confirm these findings in different contexts and with larger groups of students.
Carbon materials have been widely considered as the frameworks in lithium (Li) metal anodes due to their lightweight, high electrical conductivity, and large specific surface area. Various ...heteroatom‐doping strategies have been developed to enhance the lithiophilicity of carbon frameworks, thus rendering a uniform Li nucleation in working Li metal batteries. The corresponding lithiophilicity chemistry of doping sites has been comprehensively probed. However, various defects are inevitably introduced into carbon materials during synthesis and their critical role in regulating Li nucleation and growth behaviors is less understood. In this contribution, the defect chemistry of carbon materials in Li metal anodes is investigated through first‐principles calculations. The binding energy towards a Li atom and the critical current density are two key descriptors to reveal the defect chemistry of carbon materials. Consequently, a diagram of designing carbon frameworks with both high lithiophilicity and a large critical current density is built, from which the Stone–Wales defect is predicted to possess the best performance for delivering a uniform Li deposition. This work uncovers the defect chemistry of carbon frameworks and affords fruitful insights into defect engineering for achieving dendrite‐free Li metal anodes.
The defect chemistry of carbon materials in Li metal anodes is probed, in which the Li binding energy and the critical current density are key descriptors. A diagram of designing carbon frameworks with both high lithiophilicity and a large critical current density is built, from which the Stone–Wales defect is predicted to possess the best performance.
•A multiscale deep feature learning method with hybrid models is proposed to forecast daily inflow.•Both EEMD and FT techniques are applied for multiscale feature extraction.•A DBN is used as a deep ...feature learning approach.•Hybrid D-NNs are employed for features forecasting.•This method improves inflow forecasting accuracy due to the capacity of understanding sophisticated features sufficiently.
Inflow forecasting applies data supports for the operations and managements of reservoirs. A multiscale deep feature learning (MDFL) method with hybrid models is proposed in this paper to deal with the daily reservoir inflow forecasting. Ensemble empirical mode decomposition and Fourier spectrum are first employed to extract multiscale (trend, period and random) features, which are then represented by three deep belief networks (DBNs), respectively. The weights of each DBN are subsequently applied to initialize a neural network (D-NN). The outputs of the three-scale D-NNs are finally reconstructed using a sum-up strategy toward the forecasting results. A historical daily inflow series (from 1/1/2000 to 31/12/2012) of the Three Gorges reservoir, China, is investigated by the proposed MDFL with hybrid models. For comparison, four peer models are adopted for the same task. The results show that, the present model overwhelms all the peer models in terms of mean absolute percentage error (MAPE=11.2896%), normalized root-mean-square error (NRMSE=0.2292), determination coefficient criteria (R2=0.8905), and peak percent threshold statistics (PPTS(5)=10.0229%). The addressed method integrates the deep framework with multiscale and hybrid observations, and therefore being good at exploring sophisticated natures in the reservoir inflow forecasting.
Because traditional methods generally lack the image preprocessing link, the effect of visual image detail processing is not good. In order to enhance the image visual effect, a visual art design ...method based on virtual reality is proposed. Wavelet transform method is used to denoise the visual image and the noise signal in the image is removed; a binary model of fuzzy space vision fusion is established, the space of the visual image is planned, and the spatial distribution information of the visual image is obtained. According to the principle of light and shadow phenomenon in visual image rendering, the Extend Shadow map algorithm is used to render the visual image. Virtual reality technology was used to reconstruct the preprocessed visual image, and the ant colony algorithm was used to optimize the visual image to realize the visual image design. The results show that the peak signal-to-noise ratio of the visual image processed by the proposed method is high, and the image detail processing effect is better.
An immortal N‐(diphenylphosphanyl)‐1,3‐diisopropyl‐4,5‐dimethyl‐1,3‐dihydro‐2H‐imidazol‐2‐imine/diisobutyl (2,6‐di‐tert‐butyl‐4‐methylphenoxy) aluminum (P(NIiPr)Ph2/(BHT)AliBu2)‐based frustrated ...Lewis pair (FLP) polymerization strategy is presented for rapid and scalable synthesis of the sequence‐controlled multiblock copolymers at room temperature. Without addition of extra initiator or catalyst and complex synthetic procedure, this method enabled a tripentacontablock copolymer (n=53, k=4, dpn=50) to be achieved with the highest reported block number (n=53) and molecular weight (Mn=310 kg mol−1) within 30 min. More importantly, this FLP polymerization strategy provided access to the multiblock copolymers with tailored properties by precisely adjusting the monomer sequence and block numbers.
Non‐touching superbase: An organophosphorus superbase‐containing frustrated Lewis pair was employed to achieve sequence‐controlled tripentacontablock copolymer (n=53, k=4, dpn=50) with the highest reported block number (n=53) and molecular weight (Mn=310 kg mol−1) within 30 min.
A strong organophosphorus superbase, N‐(diphenylphosphanyl)‐1,3‐diisopropyl‐4,5‐dimethyl‐1,3‐dihydro‐2H‐imidazol‐2‐imine (IAP3) was combined with a sterically encumbered but modestly acidic Lewis ...acid (LA), (4‐Me‐2,6‐tBu2‐C6H2O)AliBu2 ((BHT)AliBu2), to synergistically promote the frustrated Lewis pair (FLP)‐catalyzed living polymerization of methyl methacrylate (MMA), achieving ultrahigh molecular weight (UHMW) poly(methyl methacrylate) (PMMA) with Mn up to 1927 kg mol−1 and narrow molecular weight distribution (MWD) at room temperature (RT). This FLP catalyst system exhibits exceptionally long lifetime polymerization performance even in the absence of free MMA, which could reinitiate the desired living polymerization after the resulting system was held at RT for 24 h.
An immortal FLP system composed of organophosporus superbase and organoaluminum promoted the living polymerization of methyl methacrylate, achieving polymers with medium to ultrahigh molecular weight (Mn up to 1927 kg mol−1) and narrow molecular weight distribution.
•A theorem of asymptotical stability is proposed for discrete fractional systems.•Implicit numerical formulae are derived and numerical solutions are obtained by use of Newton’s method.•Asymptotical ...behavior is illustrated according to the stability condition.
This study investigates stability of Caputo delta fractional difference equations. Solutions’ monotonicity and asymptotic stability of a linear fractional difference equation are discussed. A stability theorem for a discrete fractional Lyapunov direct method is proved. Furthermore, an inequality is extended from the continuous case and a sufficient condition is given. Some linear, nonlinear and time varying examples are illustrated and the results show wide prospects of the stability theorems in fractional control systems of discrete time.
Although great achievements have been made in the synthesis of giant lanthanide clusters, novel structural models are still scarce. Herein, we report a giant lanthanide cluster Dy76, constructed from ...Dy3(μ3‐OH)4 and Dy5(μ4‐O)(μ3‐OH)8 building blocks. As the largest known Dy cluster, the structure of Dy76 can be seen as arising from the fusion of two Dy48 clusters; these clusters can be isolated under various synthetic conditions and were characterized by single‐crystal X‐ray diffraction. This new, fused structural model of the pillar motif has not been found in Ln clusters. Furthermore, the successful conversion of Dy76 back into Dy48 in a retrosynthetic manner supports the proposed fusion formation mechanism of Dy76. Electrospray ionization mass spectrometry (ESI‐MS) analysis suggests that the metal cluster skeleton of Dy76 shows good stability in various solvents. This work not only reveals a new structural type of Ln clusters but also provides insight into the novel fusion assembly process.
We go together: Two dysprosium clusters with 48 and 76 metal atoms, respectively, were generated under solvothermal conditions by using 3‐furancarboxylic acid. The bi‐nanopillar Dy76 was formed by the fusion of two Dy48 nanopillars.
This study investigated the relation between corporate social responsibility (CSR) fulfillment and corporate innovation output based on a sample of 9165 Chinese listed companies. The present findings ...revealed that CSR fulfillment promoted corporate innovation output. Government subsidies and research and development investment showed significant mediating effects on the relation between CSR fulfillment and corporate innovation output. Additionally, the policy implementation of mixed ownership reform and corporate size had a significant moderating effect on this relation. Finally, the research revealed that CSR fulfillment not only promoted corporate innovation output but also improved corporate performance and the market value of a company.
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