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Two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs) offer an outstanding opportunity for spin-related technologies owing in part to their tunable structural symmetry breaking and distortions driven by organic–inorganic hydrogen (H) bonds. However, understanding how H-bonds tailor inorganic symmetry and distortions and therefore enhance spin splitting for more effective spin manipulation remains imprecise due to challenges in measuring H atom positions using X-ray diffraction. Here, we report a thermally induced structural transition (at ∼209 K) for a 2D HOIP, (2-BrPEA)2PbI4[2-BrPEA = 2-(2-bromophenyl)ethylammonium], which induces inversion asymmetry and a strong spin splitting (ΔE> 30 meV). While X-ray diffraction generally establishes heavy atom coordinates, we utilize neutron diffraction for accurate H atom position determination, demonstrating that the structural transition-induced rearrangement of H-bonds with distinct bond strengths asymmetrically shifts associated iodine atom positions. Consequences of this shift include an increased structural asymmetry, an enhanced difference between adjacent interoctahedra distortions (i.e., Pb–I–Pb bond angles), and therefore significant spin splitting. We further show that H-only density-functional theory (DFT) relaxation of the X-ray structure shifts H atoms to positions that are consistent with the neutron experimental data, validating a convenient pathway to more generally improve upon HOIP H-bonding analyses derived from quicker/less-expensive X-ray data.

Efficient utilization of solar energy for photocatalytic applications, particularly in the infrared spectrum, is crucial for addressing environmental challenges and energy scarcity. Herein we present a general strategy for constructing efficient infrared-driven photocatalysts in a metal/semiconductor heterojunction with Ohmic contact, where metals with low work function as the infrared-light absorber and semiconductors with electron storage ability can overcome the unfavorable electron flowback. Taking the NixB/MO2(M = Ce, Ti, Sn, Ge, Zr, etc.) heterojunction as an example, both experimental and theoretical investigations reveal that the formation of an Ohmic contact facilitates the transfer of hot electrons from NixB to MO2, which are stored by the ion redox pairs for the variable valence character of M. As expected, the heterojunction exhibits remarkable photocatalytic activity under infrared light (λ ≥ 800 nm), as evidenced by the efficient photofixation of CO2to high-value-added cyclic carbonates. This study offers a general platform for designing infrared-light-driven photocatalysts.

The CO2reduction into carbon-contained fuel via solar energy offers the powerful tools to realize the zero-emission carbon cycle. Owing to the intriguing features of the two-dimensional (2D) heterostructures, it is susceptible to modulate the electronic structure as well as the surface geometry for optimizing the photocatalytic CO2reactivity. From this perspective, we surveyed the fundamental insights of 2D semiconductor heterostructures, involving the fabrication strategies and classification of the 2D semiconductor heterostructure. Also, we have detailly discussed the overview of 2D semiconductor heterostructure for optimizing CO2photocatalytic influenced factors, including the solar energy utilization, photogenerated carriers separation, and redox reaction kinetics. Afterwards, we showed the significant advantages of 2D heterostructures in elevating CO2photoreduction performance, focusing on activity, selectivity and photostability. By analyzing the limitations and developments, we ended by putting forward insights into the further researches about the CO2photocatalysts and reactor design, even industrial applications.

Excitonic insulators are long-sought-after quantum materials predicted to spontaneously open a gap by the Bose condensation of bound electron–hole pairs, namely, excitons, in their ground state. Since the theoretical conjecture, extensive efforts have been devoted to pursuing excitonic insulator platforms for exploring macroscopic quantum phenomena in real materials. Reliable evidence of excitonic character has been obtained in layered chalcogenides as promising candidates. However, owing to the interference of intrinsic lattice instabilities, it is still debatable whether those features, such as the charge density wave and gap opening, are primarily driven by the excitonic effect or by the lattice transition. Herein, we develop an intercalation chemistry strategy for obtaining a novel charge-transfer excitonic insulator in organic–inorganic superlattice interfaces that serves as an ideal platform to decouple the excitonic effect from the lattice effect. In this system, we observe a narrow excitonic gap, formation of a charge density wave without periodic lattice distortion, and metal–insulator transition, providing visualized evidence of exciton condensation occurring in thermal equilibrium. Our findings identify self-assembly intercalation chemistry as a new strategy for developing novel excitonic insulators.

Electrospun nanofibrous membranes (eNFMs) have been extensively developed for bio-applications due to their structural and compositional similarity to the natural extracellular matrix. However, the emergence of antibiotic resistance in bacterial infections significantly impedes the further development and applications of eNFMs. The development of antibacterial nanomaterials substantially nourishes the engineering design of antibacterial eNFMs for combating bacterial infections without relying on antibiotics. Herein, a comprehensive review of diverse fabrication techniques for incorporating antibacterial nanomaterials into eNFMs is presented, encompassing an exhaustive introduction to various nanomaterials and their bactericidal mechanisms. Furthermore, the latest achievements and breakthroughs in the application of these antibacterial eNFMs in tissue regenerative therapy, mainly focusing on skin, bone, periodontal and tendon tissues regeneration and repair, are systematically summarized and discussed. In particular, for the treatment of skin infection wounds, we highlight the antibiotic-free antibacterial therapy strategies of antibacterial eNFMs, including (i) single model therapies such as metal ion therapy, chemodynamic therapy, photothermal therapy, and photodynamic therapy; and (ii) multi-model therapies involving arbitrary combinations of these single models. Additionally, the limitations, challenges and future opportunities of antibacterial eNFMs in biomedical applications are also discussed. We anticipate that this comprehensive review will provide novel insights for the design and utilization of antibacterial eNFMs in future research.

The rheological behavior of propellant slurries is crucial for ensuring the feasibility of the 3D printing process, controlling print quality, regulating performance, and simulating predictions. However, there have been relatively few prior studies on the rheological properties of composite solid propellant slurries at low temperatures, which hinders the application of 3D printing propellant technology under extreme temperature conditions. In addition, the use of 3D printing technology to manufacture propellants at low temperatures is advantageous for improving safety. This paper investigates the rheological properties of monodisperse systems with aluminum powder as a solid filler and end-hydroxy polybutadiene (HTPB) as the dispersed phase at low temperatures (−15∼10 °C). It explores the effects of solid content, temperature, and particle size on their rheological properties. Results show that the viscosity of the system in the range of −15∼10 °C increases exponentially with the decrease in temperature, and the viscosity at −15 °C increases by 616.90 % compared with that at 10 °C when the volume fraction (φ) of Al-1is 35.8 %; the larger size of the particles the larger the viscosity is when the temperature and φare the same, which is interpretes in terms of interfacial properties between the systems. The low-temperature correction factor is introduced into the Einstein-Roscoe equation to obtain the modified viscosity-volume fraction equation, and the correction factor is 0.0173, as evidenced by its excellent agreement with the experimental data.

Spectrum–structure correlation is playing an increasingly crucial role in spectral analysis and has undergone significant development in recent decades. With the advancement of spectrometers, the high-throughput detection triggers the explosive growth of spectral data, and the research extension from small molecules to biomolecules accompanies massive chemical space. Facing the evolving landscape of spectrum–structure correlation, conventional chemometrics becomes ill-equipped, and deep learning assisted chemometrics rapidly emerges as a flourishing approach with superior ability of extracting latent features and making precise predictions. In this review, the molecular and spectral representations and fundamental knowledge of deep learning are first introduced. We then summarize the development of how deep learning assist to establish the correlation between spectrum and molecular structure in the recent 5 years, by empowering spectral prediction (i.e., forward structure–spectrum correlation) and further enabling library matching and de novomolecular generation (i.e., inverse spectrum–structure correlation). Finally, we highlight the most important open issues persisted with corresponding potential solutions. With the fast development of deep learning, it is expected to see ultimate solution of establishing spectrum–structure correlation soon, which would trigger substantial development of various disciplines.

Water evaporation-induced electricity generators (WEGs) have drawn widespread attention in the field of hydrovoltaic technology, which can convert atmospheric thermal energy into sustainable electric power. However, it is restricted in the wide application of WEGs due to the low power output, complex fabrication process, and high cost. Herein, we present a simple and effective approach to fabricate TiO2-carbon black film-based WEGs (TC-WEGs). A single TC-WEG device can sustainably output an open-circuit voltage of 1.9 V and a maximum power density of 40.9 μW/cm2. Moreover, it has been shown that TC-WEGs exhibit stable electrical energy output when operating in seawater, which can yield a short-circuit current of 1.2 μA. The superior electricity generation performance can be attributed to the intrinsic characteristics of the TC-WEGs, including hydrophilicity, porous structure, and electrical conductivity. This work provides an important reference for the constant harvesting of clean energy.

The self-assembly of collagen within the human body creates a complex 3D fibrous network, providing structural integrity and mechanical strength to connective tissues. Recombinant collagen plays a pivotal role in the realm of biomimetic natural collagen. However, almost all of the reported recombinant collagens lack the capability of self-assembly, severely hindering their application in tissue engineering and regenerative medicine. Herein, we have for the first time constructed a series of self-assembling tyrosine-rich triple helix recombinant collagens, mimicking the structure and functionality of natural collagen. The recombinant collagen consists of a central triple-helical domain characterized by the (Gly-Xaa-Yaa)nsequence, along with N-terminal and C-terminal domains featuring the GYY sequence. The introduction of GYY has a negligible impact on the stability of the triple-helical structure of recombinant collagen while simultaneously promoting its self-assembly into fibers. In the presence of [Ru(bpy)3]Cl2and APS as catalysts, tyrosine residues in the recombinant collagen undergo covalent cross-linking, resulting in a hydrogel with exceptional mechanical properties. The recombinant collagen hydrogel exhibits outstanding biocompatibility and bioactivity, significantly enhancing the proliferation, adhesion, migration, and differentiation of HFF-1 cells. This innovative self-assembled triple-helix recombinant collagen demonstrates significant potential in the fields of tissue engineering and medical materials.

A (2,2,6,6-tetramethylpiperidin-1-yl)oxyl-mediated difunctionalization of alkenes with tert-butyl nitrite, P4S10, and alcohols has been developed for the synthesis of β-oximino phosphorodithioates. The reaction goes through a radical pathway with the successive installation of phosphorodithioate and an oxime group. This four-component protocol offers a practical approach to constructing a variety of β-oximino phosphorodithioates in moderate to good yields with favorable functional group tolerance.

Ammonia, a feedstock platform for fertilizer and pharmaceutical production, is regarded as a zero-carbon energy carrier. The electrochemical synthesis of ammonia, powered by clean and renewable electricity, has garnered increased attention as an alternative to the Haber-Bosch process. Very recently, single-atom catalysts (SACs) have become highly effective electrocatalysts for such electrochemical transformation, where the isolated metal sites ensure the high atomic utilization efficiency as well as the prevention of nitrogen-nitrogen coupling. In this review, we focus on the recent progress of single-atom catalysts in electrochemical ammonia synthesis and briefly introduce nitrogen cycles in both natural and artificial ecosystems, followed by a discussion of catalyst design by theoretical and experimental methods. Synthesis routes from different nitrogen sources, including dinitrogen (N2) and nitrogen oxides (NOx), are also highlighted. Besides, the catalysis dynamics as an indispensable section is presented and discussed in-depth. Finally, we tackle challenges and offer perspectives, aspiring to provide insightful guidance for researchers in this community striving for advanced ammonia electrosynthesis.

Electrochemical conversion of CO2and H2O to value-added products is an attractive approach for sustainable chemical production. Significant progress has been made in the past few decades in improving activity and selectivity, advancing this technology to practical application. Considering the next step for the electrochemical CO2reduction reaction, improving carbon utilisation efficiency, stability, and energy efficiency are essential. Bipolar membrane (BPM)-based electrolysers, which allow electrodes to be operated under different pH, are advantageous to tackle the challenge mentioned above. Herein, we introduced the current status of CO2electrolysers, followed by configuration, challenges, progress and outlook for combining reverse-bias BPM with different types of electrolysers. Our aim is to provide insight into developing carbon-efficient and energy-efficient CO2RR systems towards practical application.

Machine learning has been widely applied to exploring the key affecting factors for metal corrosion. However, there is a lack of appropriate algorithm for the metal corrosion in a broad region. In this paper, the atmospheric corrosion map in Hunan, China was constructed and optimized by field experiments and machine learning. Relative humidity, precipitation, SO2concentrations, NO2concentrations and PM10index were the key affecting factors of atmospheric corrosion in the region. With the identification of key affecting factors, the relative error of the ordinary least square (OLS) algorithm was lower than that of the random forest (RF) and support vector regression (SVR) algorithms. The relationship between the key affecting factors and corrosion rate was obtained by the OLS algorithm. For the tested field sites, the optimized corrosion map was constructed through the OLS algorithm, with the relative error less than 6%.

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.

Photocatalytic CH4coupling into high-valued C2H6is highly attractive, whereas the photosynthetic rate, especially under oxygen-free system, is still unsatisfying. Here, we designed the negatively charged metal supported on metal oxide nanosheets to activate the inert C–H bond in CH4and hence accelerate CH4coupling performance. As an example, the synthetic Au/ZnO porous nanosheets exhibit the C2H6photosynthetic rate of 1,121.6 µmol gcat−1h−1and the CH4conversion rate of 2,374.6 µmol gcat−1h−1under oxygen-free system, 2 orders of magnitude higher than those of previously reported photocatalysts. By virtue of several in situspectroscopic techniques, it is established that the generated Auδ−and O−species together polarized the C–H bond, while the Auδ−and O−species jointly stabilized the CH3intermediates, which favored the coupling of CH3intermediate to photosynthesize C2H6instead of overoxidation into COx. Thus, the design of dual active species is beneficial for achieving high-efficient CH4-to-C2H6photoconversion.

The practical application of the H2/O2proton-exchange membrane fuel cell (PEMFC) is being greatly limited by the use of high-cost Pt as electrode catalysts. Furthermore, the H2/O2PEMFC is nonrechargeable and thus precludes kinetics energy recovery when equipped on electric vehicles and peak power regulation when combined to power grids. Here, we demonstrate a rechargeable H2/O2PEMFC through embedding a redox flow battery into a conventional H2/O2PEMFC. This flow battery employs H2/O2reactive redox pairs such as NO3–/NO-Br2/Br–and H4SiW12O40/H5SiW12O40whose redox potentials are as close as possible to those of O2/H2O and H2/H2O, respectively, so that the chemical potential losses during their reactions with O2at the cathode and H2at the anode were minimized. More importantly, the electrochemical reversibility allows the H2/O2reacted redox pairs to be easily regenerated through fuel cell discharging on catalyst-free carbon electrodes at a low overpotential and brings in the fuel cell both chemical and electrical rechargeability, thereby realizing integrated functions of electricity generation- storage as well as efficient operation (achieving an open-circuit potential of 0.96 V and a peak power density of 0.57 W/cm2, which are comparable to a conventional H2/air PEMFC) with catalyst-free carbon electrodes.

This study proposes a self-adaptive multistage constant current (SAMCC) fast-charging strategy for a battery at high ambient temperatures (40 °C). This strategy contains an electrothermal-degradation model for the battery and integrates a balanced cooling strategy. To realize the self-adaption of the algorithm, the genetic algorithm is used to determine the stage number of the charging strategy and the current in each stage. To balance the charging time and the capacity loss, the Pareto curve is adopted. Then, the SAMCC strategy is used for a 50-Ah prismatic battery. According to the results, the constraint condition of $U_{\mathrm {b}} \le4.2$ V and the small currents in the second, third, and fourth stages make the SAMCC avoid over-charging, and the active cooling strategy eliminates overheating in the battery. At high ambient temperatures, the terminal voltage of the battery charged by the SAMCC never goes over 4.2 V, and its maximum temperature is 43 °C. Moreover, the SAMCC is compared with the standard fast-charging strategy (SFC) to prove its superiority. Its charging time tested at 40 °C is 644 s shorter than that of the SFC, and its state of health (SOH) is only 0.12% lower than that of the SFC at the cycle number of 100.

In recent years, research on spin ordering phenomena has broadened to include intermolecular exchange interactions in organic–inorganic crystals and goes beyond the traditional focus on interatomic interactions. However, a crystallized framework used for stabilizing parallel spin alignment through an ordered lattice is indispensable to ferromagnetism. Here we demonstrate room-temperature ferromagnetic order in two-dimensional confined molecule-based monolayers. The confinement effect of the van der Waals interlayer space enables cobaltocene molecules to self-assemble into a monolayer with a honeycomb-like configuration. The spontaneous uniform spin orientation—ferromagnetic coupling—is established by an intermolecular vibronic superexchange interaction, involving a cooperative dynamic Jahn–Teller effect in the confined cobaltocene monolayer. The molecular cobaltocene monolayers exhibit a ferromagnetic transition temperature above room temperature and have a large saturation magnetization.

Lithium-ion batteries are crucial power sources in many industries. When they are used at low temperatures, their performance decreases greatly. Thus, heating is required. This article proposes a health-aware heating strategy based on the ac current. The strategy combines the use of the electro-thermal model for predicting the temperature increment in the battery caused by the ac current and a diagnostic method for lithium plating based on the charging transfer impedance. To obtain an accurate electro-thermal model for ac heating, various equivalent circuit models (ECMs) are tested at pulses with different frequencies and amplitudes, and the second-order ECM with a constant phase element is found to obtain the smallest error. Then, the model and the lithium plating criterion are used to establish the boundary map of the lithium plating and voltage limitations. Based on the map, the optimal ac frequency and amplitude are selected for the heating strategy. Finally, an ac heating experiment is implemented to verify the strategy. The results show that the strategy not only increases the battery temperature at a maximum rate of 5.25 °C/min, but also achieves a capacity decrease of 0.3% in 80 heating cycles. Moreover, according to the battery disassembly results, there is no lithium plating on the electrode. These results prove that the proposed heating strategy can prevent lithium plating and achieve a high heating efficiency.