Covalent Organic Frameworks for Photocatalytic Reduction of Carbon Dioxide: A Review.

Covalent organic frameworks (COFs) are crystalline networks with extended backbones cross-linked by covalent bonds. Due to the semiconductive properties and variable metal coordinating sites, along with the rapid development in linkage chemistry, the utilization of COFs in photocatalytic CO2RR has attracted many scientists' interests. In this Review, we summarize the latest research progress on variable COFs for photocatalytic CO2 reduction. In the first part, we present the development of COF linkages that have been used in CO2RR, and we discuss four mechanisms including COFs as intrinsic photocatalysts, COFs with photosensitive motifs as photocatalysts, metalated COF photocatalysts, and COFs with semiconductors as heterojunction photocatalysts. Then, we summarize the principles of structural designs including functional building units and stacking mode exchange. Finally, the outlook and challenges have been provided. This Review is intended to give some guidance on the design and synthesis of diverse COFs with different linkages, various structures, and divergent stacking modes for the efficient photoreduction of CO2.

Crystal structure of 4-bromo-5,7-dimeth-oxy-2,3-di-hydro-1H-inden-1-one.

In the title mol-ecule, C11H11BrO3, the di-hydro-indene moiety is essentially planar but with a slight twist in the saturated portion of the five-membered ring. The meth-oxy groups lie close to the above plane. In the crystal, π-stacking inter-actions between six-membered rings form stacks of mol-ecules extending along the a-axis direction, which are linked by weak C-H⋯O and C-H⋯Br hydrogen bonds. A Hirshfeld surface analysis was performed showing H⋯H, O⋯H/H⋯O and Br⋯H/H⋯Br contacts make the largest contributions to inter-molecular inter-actions in the crystal.

A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.

Background: Carotid atherosclerosis (CAS) is a significant risk factor for cardio-cerebrovascular events. The objective of this study is to employ stacking ensemble machine learning techniques to enhance the prediction of CAS occurrence, incorporating a wide range of predictors, including endocrine-related markers. Methods: Based on data from a routine health check-up cohort, five individual prediction models for CAS were established based on logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost) and gradient boosting decision tree (GBDT) methods. Then, a stacking ensemble algorithm was used to integrate the base models to improve the prediction ability and address overfitting problems. Finally, the SHAP value method was applied for an in-depth analysis of variable importance at both the overall and individual levels, with a focus on elucidating the impact of endocrine-related variables. Results: A total of 441 of the 1669 subjects in the cohort were finally diagnosed with CAS. Seventeen variables were selected as predictors. The ensemble model outperformed the individual models, with AUCs of 0.893 in the testing set and 0.861 in the validation set. The ensemble model has the optimal accuracy, precision, recall and F1 score in the validation set, with considerable performance in the testing set. Carotid stenosis and age emerged as the most significant predictors, alongside notable contributions from endocrine-related factors. Conclusion: The ensemble model shows enhanced accuracy and generalizability in predicting CAS risk, underscoring its utility in identifying individuals at high risk. This approach integrates a comprehensive analysis of predictors, including endocrine markers, affirming the critical role of endocrine dysfunctions in CAS development. It represents a promising tool in identifying high-risk individuals for the prevention of CAS and cardio-cerebrovascular diseases.

Clonal gamete-mediated polyploid genome design for stacking genomes.

Hybrid vigor in plants confers better agronomically significant traits in offspring compared with either parent. Recently, Wang et al. reported a mitosis instead of meiosis (MiMe) system in tomato for clonal gamete production, showing the potential to exploit autopolyploid progressive heterosis by stacking genomes from four grandparents in tetraploid hybrids, developed from crossing MiMe hybrids.

Regional ecological risk assessment through the application of production-living-ecology land use function indicators: a case study of the eco-economic zone of Yellow River Delta.

To date, land use structure information has been employed extensively for ecological risk assessment (ERA) purpose in regional/landscape scales; in contrast, land use function (LUF) information-based ERA research is still scarce. Therefore, it is necessary to carry out more ERA case studies in macroscale with the help of pertinent LUF indicators. As an important way to construct production-living-ecology LUF indexes, this study employs the weighted stacking method and related economic statistical data for regional ecological risk assessment (RERA) purpose within Yellow River Delta High-efficiency Eco-economic Zone (YRDHEZ), China. This YRDHEZ-RERA research pointed out that (1) it was rational to use a series of economic statistical data to more comprehensively and precisely characterize regional production and living function grades in YRDHEZ. (2) The Yellow River Delta had lower agriculture and non-agriculture production functions, whereas the rest of the zone had higher production functions. (3) Most people lived in the south part, whereas north coastal zone had very low population density; the east part had higher per capita disposable income of urban/rural households than that of west. (4) The south part of the zone had higher production/living functions and integrated ecological risk source intensity than those of north coastal zone, whereas the coastal zone had higher ecology function, eco-environmental vulnerability, and final integrated ecological risk than those of inland region. As for regional ecological risk management, establishing nature reserve with strict spatial governance for coastal/estuarine wetlands and coordinating production/ecology functions of coastal salterns/breeding ponds are relevant feasible measures.

Predicting Long COVID in the National COVID Cohort Collaborative Using Super Learner: Cohort Study.

BACKGROUND: Postacute sequelae of COVID-19 (PASC), also known as long COVID, is a broad grouping of a range of long-term symptoms following acute COVID-19. These symptoms can occur across a range of biological systems, leading to challenges in determining risk factors for PASC and the causal etiology of this disorder. An understanding of characteristics that are predictive of future PASC is valuable, as this can inform the identification of high-risk individuals and future preventative efforts. However, current knowledge regarding PASC risk factors is limited. OBJECTIVE: Using a sample of 55,257 patients (at a ratio of 1 patient with PASC to 4 matched controls) from the National COVID Cohort Collaborative, as part of the National Institutes of Health Long COVID Computational Challenge, we sought to predict individual risk of PASC diagnosis from a curated set of clinically informed covariates. The National COVID Cohort Collaborative includes electronic health records for more than 22 million patients from 84 sites across the United States. METHODS: We predicted individual PASC status, given covariate information, using Super Learner (an ensemble machine learning algorithm also known as stacking) to learn the optimal combination of gradient boosting and random forest algorithms to maximize the area under the receiver operator curve. We evaluated variable importance (Shapley values) based on 3 levels: individual features, temporal windows, and clinical domains. We externally validated these findings using a holdout set of randomly selected study sites. RESULTS: We were able to predict individual PASC diagnoses accurately (area under the curve 0.874). The individual features of the length of observation period, number of health care interactions during acute COVID-19, and viral lower respiratory infection were the most predictive of subsequent PASC diagnosis. Temporally, we found that baseline characteristics were the most predictive of future PASC diagnosis, compared with characteristics immediately before, during, or after acute COVID-19. We found that the clinical domains of health care use, demographics or anthropometry, and respiratory factors were the most predictive of PASC diagnosis. CONCLUSIONS: The methods outlined here provide an open-source, applied example of using Super Learner to predict PASC status using electronic health record data, which can be replicated across a variety of settings. Across individual predictors and clinical domains, we consistently found that factors related to health care use were the strongest predictors of PASC diagnosis. This indicates that any observational studies using PASC diagnosis as a primary outcome must rigorously account for heterogeneous health care use. Our temporal findings support the hypothesis that clinicians may be able to accurately assess the risk of PASC in patients before acute COVID-19 diagnosis, which could improve early interventions and preventive care. Our findings also highlight the importance of respiratory characteristics in PASC risk assessment. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1101/2023.07.27.23293272.

Sulfur and Wavy-Stacking Boosted Superior Lithium Storage in 2D Covalent Organic Frameworks.

2D conjugated covalent organic frameworks (c-COFs) provide an attractive foundation as organic electrodes in energy storage devices, but their storage capability is long hindered by limited ion accessibility within densely π-π stacked interlayers. Herein, two kinds of 2D c-COFs based on dioxin and dithiine linkages are reported, which exhibit distinct in-plane configurations-fully planar and undulated layers. X-ray diffraction analysis reveals wavy square-planar networks in dithiine-bridged COF (COF-S), attributed to curved C─S─C bonds in the dithiine linkage, whereas dioxin-bridged COF (COF-O) features densely packed fully planar layers. Theoretical and experimental results elucidate that the undulated stacking within COF-S possesses an expanded layer distance of 3.8 Å and facilitates effective and rapid Li+ storage, yielding a superior specific capacity of 1305 mAh g-1 at 0.5 A g-1, surpassing that of COF-O (1180 mAh g-1 at 0.5 A g-1). COF-S also demonstrates an admirable cycle life with 80.4% capacity retention after 5000 cycles. As determined, self-expanded wavy-stacking geometry, S-enriched dithiine in COF-S enhances the accessibility and redox activity of Li storage, allowing each phthalocyanine core to store 12 Li+ compared to 8 Li+ in COF-O. These findings underscore the elements and stacking modes of 2D c-COFs, enabling tunable layer distance and modulation of accessible ions.

Highly sensitive two-dimensional profiling of N-linked glycans by hydrophilic interaction liquid chromatography and dual stacking capillary gel electrophoresis.

BACKGROUND: N-Glycosylation is one of the most important post-translational modifications in proteins. As the N-glycan profiles in biological samples are diverse and change according to the pathological condition, various profiling methods have been developed, such as liquid chromatography (LC), capillary electrophoresis (CE), and mass spectrometry. However, conventional analytical methods have limitations in sensitivity and/or resolution, hindering the discovery of minor but specific N-glycans that are important both in the basic glycobiology research and in the medical application as biomarkers. Therefore, a highly sensitive and high-resolution N-glycan profiling method is required. RESULTS: In this study, we developed a novel two-dimensional (2D) separation system, which couples hydrophilic interaction liquid chromatography (HILIC) with capillary gel electrophoresis (CGE) via large-volume dual preconcentration by isotachophoresis and stacking (LDIS). Owing to the efficient preconcentration efficiency of LDIS, limit of detection reached 12 pM (60 amol, S/N = 3) with good calibration curve linearity (R2 > 0.999) in the 2D analysis of maltoheptaose. Finally, 2D profiling of N-glycans obtained from standard glycoproteins and cell lysates were demonstrated. High-resolution 2D profiles were successfully obtained by data alignment using triple internal standards. N-glycans were well distributed on the HILIC/CGE 2D plane based on the glycan size, number of sialic acids, linkage type, and so on. As a result, specific minor glycans were successfully identified in HepG2 and HeLa cell lysates. SIGNIFICANCE AND NOVELTY: In conclusion, the HILIC/CGE 2D analysis method showed sufficient sensitivity and resolution for identifying minor but specific N-glycans from complicated cellular samples, indicating the potential as a next-generation N-glycomics tool. Our novel approach for coupling LC and CE can also dramatically improve the sensitivity in other separation modes, which can be a new standard of 2D bioanalysis applicable not only to glycans, but also to other diverse biomolecules such as metabolites, proteins, and nucleic acids.

Development of a dispersive solid phase microextraction method based on the application of MgAl, NiAl, and CoAl-layered double hydroxides for the efficient removal of α- and ß-naphthol isomers from water samples by capillary electrophoresis.

The present work describes a quick, simple, and efficient method based on the use of layered double hydroxides (LDH) coupled to dispersive solid phase micro-extraction (DSPME) to remove α-naphthol (α-NAP) and ß-naphthol (ß-NAP) isomers from water samples. Three different LDHs (MgAl-LDH, NiAl-LDH, and CoAl-LDH) were used to study how the interlayer anion and molar ratio affected the removal performance. The critical factors in the DSPME procedure (pH, LDH amount, contact time) were optimized by the univariate method under the optimal conditions: pH, 4-8; LDH amount, 5 mg; and contact time, 2.5 min. The method can be successfully applied in real sample waters, removing NAP isomers even in ultra-trace concentrations. The large volume sample stacking (LVSS-CE) technique provides limits of detections (LODs) of 5.52 µg/L and 6.36 µg/L for α-naphthol and ß-naphthol, respectively. The methodology's precision was evaluated on intra- and inter-day repeatability, with %RSD less than 10% in all cases. The MgAl/Cl--LDH selectivity was tested in the presence of phenol and bisphenol A, with a removal rate of >92.80%. The elution tests suggest that the LDH MgAl/Cl--LDH could be suitable for pre-concentration of α-naphthol and ß-naphthol in future works.

Revisiting the Most Stable Structures of the Benzene Dimer.

The benzene dimer (BD) is an archetypal model of π∙∙∙π and C-H∙∙∙π noncovalent interactions as they occur in its cofacial and perpendicular arrangements, respectively. The enthalpic stabilization of the related BD structures has been debated for a long time and is revisited here. The revisit is based on results of computations that apply the coupled-cluster theory with singles, doubles and perturbative triples [CCSD(T)] together with large basis sets and extrapolate results to the complete basis set (CBS) limit in order to accurately characterize the three most important stationary points of the intermolecular interaction energy (ΔE) surface of the BD, which correspond to the tilted T-shaped (TT), fully symmetric T-shaped (FT) and slipped-parallel (SP) structures. In the optimal geometries obtained by searching extensive sets of the CCSD(T)/CBS ΔE data of the TT, FT and SP arrangements, the resulting ΔE values were -11.84, -11.34 and -11.21 kJ/mol, respectively. The intrinsic strength of the intermolecular bonding in these configurations was evaluated by analyzing the distance dependence of the CCSD(T)/CBS ΔE data over wide ranges of intermonomer separations. In this way, regions of the relative distances that favor BD structures with either π∙∙∙π or C-H∙∙∙π interactions were found and discussed in a broader context.

Self-Anchored Van-Der-Waals Stacking Growth of Transition-Metal Dichalcogenide Nanoplates.

Transition-metal dichalcogenide (TMDs) nanoplates exhibit unique properties different from their monolayer counterparts. Controllable nucleation and growth are prerequisite and highly desirable for their practical applications. Here, a self-anchored van-der-Waals stacking growth method is developed, by which the substrate pit induced by precursor etching anchors the source material, impedes the lateral spreading of source droplets and facilitates the in situ stacking growth of high-quality TMD nanoplates with a thickness of tens to hundreds of nanometers at well-defined locations. As such, an array of TMD nanoplates with controlled lateral dimensions are produced and applied in arrayed photodetectors. This study solves the problem of controllable preparation of TMD nanoplates, holding promise for applications in electronics and optoelectronics.

Comprehending the Efficacy of Whitlock's Caffeine-Pincered Molecular Tweezer on ß-Amyloid Aggregation.

Alzheimer's disease (AD) stands as one of the most prevalent neurodegenerative conditions, leading to cognitive impairment, with no cure and preventive measures. Misfolding and aberrant aggregation of amyloid-ß (Aß) peptides are believed to be the underlying cause of AD. These amyloid aggregates culminate in the development of toxic Aß oligomers and subsequent accumulation of ß-amyloid plaques amidst neuronal cells in the brain, marking the hallmarks of AD. Drug development for the potentially curative treatment of Alzheimer's is, therefore, a tremendous challenge for the scientific community. In this study, we investigate the potency of Whitlock's caffeine-armed molecular tweezer in combating the deleterious effects of Aß aggregation, with special emphasis on the seven residue Aß16-22 fragment. Extensive all-atom molecular dynamics simulations are conducted to probe the various structural and conformational transitions of the peptides in an aqueous medium in both the presence and absence of tweezers. To explore the specifics of peptide-tweezer interactions, radial distribution functions, contact number calculations, binding free energies, and 2-D kernel density plots depicting the variation of distance-angle between the aromatic planes of the peptide-tweezer pair are computed. The central hydrophobic core, particularly the aromatic Phe residues, is crucial in the development of harmful amyloid oligomers. Notably, all analyses indicate reduced interpeptide interactions in the presence of the tweezer, which is attributed to the tweezer-Phe aromatic interaction. Upon increasing the tweezer concentration, the residues of the peptide are further encased in a hydrophobic environment created by the self-aggregating tweezer cluster, leading to the segregation of the peptide residues. This is further aided by the weakening of interstrand hydrogen bonding between the peptides, thereby impeding their self-aggregation and preventing the formation of neurotoxic ß-amyloid. Furthermore, the study also highlights the efficacy of the molecular tweezer in destabilizing preformed amyloid fibrils as well as hindering the aggregation of the full-length Aß1-42 peptide.

Long-Range π-π Stacking Brings High Electron Delocalization for Enhanced Photocatalytic Activity in Hydrogen-Bonded Organic Framework.

Unlike many studies that regulate transport and separation behaviour of photogenerated charge carriers through controlling the chemical composite, our work demonstrates this goal can be achieved through simply tuning the molecular π-π packing from short-range to long-range within hydrogen-bonded organic frameworks (HOFs) without altering the building blocks or network topology. Further investigations reveal that the long-range π-π stacking significantly promotes electron delocalization and enhances electron density, thereby effectively suppressing electron-hole recombination and augmenting the charge transfer rate. Simultaneously, acting as a porous substrate, it boosts electron density of Pd nanoparticle loaded on its surfaces, resulting in remarkable CO2 photoreduction catalytic activity (CO generation rate: 48.1 µmol/g/h) without the need for hole scavengers. Our study provide insight into regulating the charge carrier behaviours in molecular assemblies based on hydrogen bonds, offering a new clue for efficient photocatalyst design.

Radio Signal Modulation Recognition Method Based on Hybrid Feature and Ensemble Learning: For Radar and Jamming Signals.

The detection performance of radar is significantly impaired by active jamming and mutual interference from other radars. This paper proposes a radio signal modulation recognition method to accurately recognize these signals, which helps in the jamming cancellation decisions. Based on the ensemble learning stacking algorithm improved by meta-feature enhancement, the proposed method adopts random forests, K-nearest neighbors, and Gaussian naive Bayes as the base-learners, with logistic regression serving as the meta-learner. It takes the multi-domain features of signals as input, which include time-domain features including fuzzy entropy, slope entropy, and Hjorth parameters; frequency-domain features, including spectral entropy; and fractal-domain features, including fractal dimension. The simulation experiment, including seven common signal types of radar and active jamming, was performed for the effectiveness validation and performance evaluation. Results proved the proposed method's performance superiority to other classification methods, as well as its ability to meet the requirements of low signal-to-noise ratio and few-shot learning.

A feasibility study of tumor motion monitoring for SBRT of lung cancer based on 3D point cloud detection and stacking ensemble learning.

PURPOSE: To construct a tumor motion monitoring model for stereotactic body radiation therapy (SBRT) of lung cancer from a feasibility perspective. METHODS: A total of 32 treatment plans for 22 patients were collected, whose planning CT and the centroid position of the planning target volume (PTV) were used as the reference. Images of different respiratory phases in 4DCT were acquired to redefine the targets and obtain the floating PTV centroid positions. In accordance with the planning CT and CBCT registration parameters, data augmentation was accomplished, yielding 2130 experimental recordings for analysis. We employed a stacking multi-learning ensemble approach to fit the 3D point cloud variations of body surface and the change of target position to construct the tumor motion monitoring model, and the prediction accuracy was assess using root mean squared error (RMSE) and R-Square (R2). RESULTS: The prediction displacement of the stacking ensemble model shows a high degree of agreement with the reference value in each direction. In the first layer of model, the X direction (RMSE =0.019 ∼ 0.145mm, R2 =0.9793∼0.9996) and the Z direction (RMSE = 0.051 ∼ 0.168 mm, R2 = 0.9736∼0.9976) show the best results, while the Y direction ranked behind (RMSE = 0.088 ∼ 0.224 mm, R2 = 0.9553∼ 0.9933). The second layer model summarizes the advantages of unit models of first layer, and RMSE of 0.015 mm, 0.083 mm, 0.041 mm, and R2 of 0.9998, 0.9931, 0.9984 respectively for X, Y, Z were obtained. CONCLUSIONS: The tumor motion monitoring method for SBRT of lung cancer has potential application of non-ionization, non-invasive, markerless, and real-time.

Two Different Chiral Groups Based Thermally Activated Delayed Fluorescence Materials for Circularly Polarized OLEDs.

Circularly polarized organic light-emitting diodes (CP-OLEDs) hold significant promise for applications in 3D displays due to the ability to generate circularly polarized luminescence (CPL) directly. In this study, two pairs of circularly polarized thermally activated delayed fluorescence (CP-TADF) enantiomers, named RR/SS-ONCN and RS/SR-ONCN, were synthesized by integrating two distinct chiral groups into the dicyanobenzene unit. The RR/SS-ONCN and RS/SR-ONCN enantiomers show CPL properties with dissymmetry photoluminescence factors (|gPL|) of 1.3 × 10-3 and 2.0 × 10-3 in doped films, respectively. Notably, RR/SS-ONCN exhibit higher |gPL| values than that of RS/SR-ONCN, especially in doped films, indicating that when the configurations of the two chiral groups are identical, the |gPL| value of the CP-TADF materials can be enhanced, demonstrating a certain stacking effect. Moreover, the corresponding CP-OLEDs demonstrate good performances, achieving maximum external quantum efficiencies of up to 21.9% and notable CP electroluminescence with |gEL| factors of up to 1.0 × 10-3.

Constructing Slip Stacking Diversity in Van der Waals Homobilayers.

The van der Waals (vdW) interface provides two important degrees of freedom-twist and slip-to tune interlayer structures and inspire unique physics. However, constructing diversified high-quality slip stackings (i.e., lattice orientations between layers are parallel with only interlayer sliding) is more challenging than twisted stackings due to angstrom-scale structural discrepancies between different slip stackings, sparsity of thermodynamically stable candidates and insufficient mechanism understanding. Here, using transition metal dichalcogenide (TMD) homobilayers as a model system, this work theoretically elucidates that vdW materials with low lattice symmetry and weak interlayer coupling allow the creation of multifarious thermodynamically advantageous slip stackings, and experimentally achieves 13 and 9 slip stackings in 1T″-ReS2 and 1T″-ReSe2 bilayers via direct growth, which are systematically revealed by atomic-resolution scanning transmission electron microscopy (STEM), angle-resolved polarization Raman spectroscopy, and second harmonic generation (SHG) measurements. This work also develops modulation strategies to switch the stacking via grain boundaries (GBs) and to expand the slip stacking library from thermodynamic to kinetically favored structures via in situ thermal treatment. Finally, density functional theory (DFT) calculations suggest a prominent dependence of the pressure-induced electronic band structure transition on stacking configurations. These studies unveil a unique vdW epitaxy and offer a viable means for manipulating interlayer atomic registries.

A hybrid model for the classification of Autism Spectrum Disorder using Mu rhythm in EEG.

BACKGROUND: Autism Spectrum Disorder (ASD) is a condition with social interaction, communication, and behavioral difficulties. Diagnostic methods mostly rely on subjective evaluations and can lack objectivity. In this research Machine learning (ML) and deep learning (DL) techniques are used to enhance ASD classification. OBJECTIVE: This study focuses on improving ASD and TD classification accuracy with a minimal number of EEG channels. ML and DL models are used with EEG data, including Mu Rhythm from the Sensory Motor Cortex (SMC) for classification. METHODS: Non-linear features in time and frequency domains are extracted and ML models are applied for classification. The EEG 1D data is transformed into images using Independent Component Analysis-Second Order Blind Identification (ICA-SOBI), Spectrogram, and Continuous Wavelet Transform (CWT). RESULTS: Stacking Classifier employed with non-linear features yields precision, recall, F1-score, and accuracy rates of 78%, 79%, 78%, and 78% respectively. Including entropy and fuzzy entropy features further improves accuracy to 81.4%. In addition, DL models, employing SOBI, CWT, and spectrogram plots, achieve precision, recall, F1-score, and accuracy of 75%, 75%, 74%, and 75% respectively. The hybrid model, which combined deep learning features from spectrogram and CWT with machine learning, exhibits prominent improvement, attained precision, recall, F1-score, and accuracy of 94%, 94%, 94%, and 94% respectively. Incorporating entropy and fuzzy entropy features further improved the accuracy to 96.9%. CONCLUSIONS: This study underscores the potential of ML and DL techniques in improving the classification of ASD and TD individuals, particularly when utilizing a minimal set of EEG channels.

Axially Growing Carbon Quantum Ribbon with 2D Stacking Control for High-Stability Solar Cell.

Although power conversion efficiency (PCE) of solar cells (SCs) continues to improve, they are still far from practical application because of their complex synthesis process, high cost and inferior operational stability. Carbon quantum dots with high material stability and remarkable photoluminescence are successfully used in light-emitting diodes. A good light emitter should also be an efficient SC according to the photon balance in Shockley-Quieisser formulation, in which all excitons are ultimately separated. However, the finite quantum-sized sp2 domain leads to tight exciton bonding, and highly delocalized electron clouds in irregular molecular stacks form disordered charge transfer, resulting in severe energy loss. Herein, an axially growing carbon quantum ribbon (AG-CQR) with a wide optical absorption range of 440-850 nm is reported. Structural and computational studies reveal that AG-CQRs (aspect ratio ≈2:1) with carbonyl groups at both ends regulate energy level and efficiently separate excitons. The stacking-controlled two-dimensional AG-CQR film further directionally transfers electrons and holes, particularly in AB stacking mode. Using this film as active layer alone, the SCs yield a maximum PCE of 1.22%, impressive long-term operational stability of 380 h, and repeatability. This study opens the door for the development of new-generation carbon-nanomaterial-based SCs for practical applications.

Novel in-situ encapsulation of tin phosphide particles in MXene conductive networks as anode materials of the durable sodium-ion battery.

Sodium-ion battery (SIB) is one of potential alternatives to lithium-ion battery, because of abundant resources and lower price of sodium. High electrical conductivity and long-term durability of MXene are advantageous as the anode material of SIB, but low energy density restricts applications. Tin phosphide possesses high theoretical capacity, low redox potential, and large energy density, but volume expansion reduces its cycling stability. In this study, tin phosphide particles are in-situ encapsulated into MXene conductive networks (SnxPy/MXene) by hydrothermal and phosphorization processes as novel anode materials of SIB. MXene amounts and hydrothermal durations are investigated to evenly distribute SnxPy in MXene. After 100 cycles, SnxPy/MXene reaches high specific capacities of 438.8 and 314.1 mAh/g at 0.2 and 1.0 A/g, respectively. The capacity retentions of 6.0% and 73.6% at 0.2 A/g are respectively obtained by SnxPy and SnxPy/MXene. The better specific capacity and cycling stability of SnxPy/MXene are attributed to less volume expansion of SnxPy during charge/discharge processes and relieved self-stacking of MXene by encapsulating SnxPy particles between MXene layers. Electrochemical impedance spectroscopy and Galvanostatic intermittent titration technique are also applied to analyze the charge storage mechanism in SIB. Higher sodium ion diffusion coefficient and smaller charge-transfer resistance are obtained by SnxPy/MXene.