Guest-induced (crystal-to-crystal) transformation, i.e., periodic flexibility, is a typical feature of molecule-based crystalline porous materials, but its role for adsorptive separation is controversial. On the other hand, aperiodic flexibility is rarely studied. This work reports a pair of isomeric Cu(I) triazolate frameworks, namely, α-[Cu(fetz)] (MAF-2Fa) and ß-[Cu(fetz)] (MAF-2Fb), which show typical periodic and aperiodic flexibility for CO chemical adsorption, respectively. Quantitative mixture breakthrough experiments show that, while MAF-2Fa exhibits high adsorption capacity at high pressures but negligible adsorption below the threshold pressure and with leakage concentrations of 3-8%, MAF-2Fb exhibits relatively low adsorption capacity at high pressures but no leakage (residual CO concentration <1 ppb). Tandem connection of MAF-2Fa and MAF-2Fb can combine their advantages of high CO adsorption capacities at high and low pressures, respectively. MAF-2Fa and MAF-2Fb can both keep the separation performances unchanged at high relative humidities, but only MAF-2Fb shows a unique coadsorption behavior at a relative humidity of 82%, which can be used to improve purification performances.
BACKGROUND: Changes in healthy and inflamed pulp on periapical radiographs are traditionally so subtle that they may be imperceptible to human experts, limiting its potential use as an adjunct clinical diagnostic feature. AIM: This study aimed to investigate the feasibility of an image-analysis technique based on the convolutional neural network (CNN) to detect irreversible pulpitis in primary molars on periapical radiographs (PRs). DESIGN: This retrospective study was performed in two health centres. Patients who received indirect pulp therapy at Peking University Hospital for Stomatology were retrospectively identified and randomly divided into training and validation sets (8:2). Using PRs as input to an EfficientNet CNN, the model was trained to categorise cases into either the success or failure group and externally tested on patients who presented to our affiliate institution. Model performance was evaluated using sensitivity, specificity, accuracy and F1 score. RESULTS: A total of 348 PRs with deep caries were enrolled from the two centres. The deep learning model achieved the highest accuracy of 0.90 (95% confidence interval: 0.79-0.96) in the internal validation set, with an overall accuracy of 0.85 in the external test set. The mean greyscale value was higher in the failure group than in the success group (p = .013). CONCLUSION: The deep learning-based model could detect irreversible pulpitis in primary molars with deep caries on PRs. Moreover, this study provides a convenient and complementary method for assessing pulp status.
OBJECTIVES: This study proposes a chairside digital design and manufacturing method for band and loop space maintainers and preliminarily validates its clinical feasibility. METHODS: Clinical cases of 10 children requiring space maintenance caused by premature loss of primary teeth were collected. Intraoral scan data of the affected children were also collected to establish digital models of the missing teeth. Using a pediatric band and loop space maintainer design software developed by our research team, a rapid personalized design of band and loop structures was achieved, and a digital model of an integrated band and loop space maintainer was ultimately generated. A chairside space maintainer was manufactured through metal computer numerical control machining for the experimental group, whereas metal 3D printing in the dental laboratory was used for the control group. A model fitting assessment was conducted for the space maintainers of both groups, and senior pediatric dental experts were invited to evaluate the clinical feasibility of the space maintainers with regard to fit and stability using the visual analogue scale scoring system. Statistical analysis was also performed. RESULTS: The time spent in designing and manufacturing the 10 space maintainers of the experimental group was all less than 1 h. Statistical analysis of expert ratings showed that the experimental group outperformed the control group with regard to fit and stability. Both types of space maintainers met clinical requirements. CONCLUSIONS: The chairside digital design and manufacturing method for pediatric band and loop space maintainers proposed in this study can achieve same-day fitting of space maintainers at the first appointment, demonstrating good clinical feasibility and significant potential for clinical application.
Tooth Loss , Humans , Child , Printing, Three-Dimensional , Space Maintenance, Orthodontic , Computer-Aided Design
Ferroelastic materials have gained widespread attention as promising candidates for mechanical switches, shape memory, and information processing. Their phase-transition mechanisms usually originate from conventional order-disorder and/or displacive types, while those involving dynamic coordination bonds are still scarce. Herein, based on a strategic molecular design of organic cations, we report three new polar hybrid crystals with a generic formula of AA'RbBiCl6 (A = A' = Me3SO+ for 1; A = Me3SO+ and A' = Me4N+ for 2; A = A' = Me3NNH2+ for 3). Their A-site cations link to the [RbBiCl6]n2n- inorganic framework with lon topology through Rb-O/N coordination bonds, while their significantly different interactions between A'-site cations and inorganic frameworks provide distinct phase-transition behaviour. In detail, the strongly coordinative A'-site Me3SO+ cations prevent 1 from a structural phase transition, while coordinatively free A'-site Me4N+ cations trigger a conventional order-disorder ferroelastic transition at 247 K in 2, accompanied by a latent heat of 0.63 J g-1 and a usual "high â low" second-harmonic-generation (SHG) switch. Interestingly, the A'-site Me3NNH2+ cations in 3 reveal unusual dynamic coordination bonds, driving a high-temperature ferroelastic transition at 369 K with a large latent heat of 18.34 J g-1 and an unusual "low â high" SHG-switching behaviour. This work provides an effective molecular assembly strategy to establish dynamic coordination bonds in a new type of host-guest model and opens an avenue for designing advanced ferroelastic multifunctional materials.
Carbon monoxide (CO) separation relies on chemical adsorption but suffers from the difficulty of desorption and instability of open metal sites against O2, H2O and so on. Here we demonstrate quasi-open metal sites with hidden or shielded coordination sites as a promising solution. Possessing the trigonal coordination geometry (sp2), Cu(I) ions in porous frameworks show weak physical adsorption for non-target guests. Rational regulation of framework flexibility enables geometry transformation to tetrahedral geometry (sp3), generating a fourth coordination site for the chemical adsorption of CO. Quantitative breakthrough experiments at ambient conditions show CO uptakes up to 4.1 mmol g-1 and CO selectivity up to 347 against CO2, CH4, O2, N2 and H2. The adsorbents can be completely regenerated at 333-373 K to recover CO with a purity of >99.99%, and the separation performances are stable in high-concentration O2 and H2O. Although CO leakage concentration generally follows the structural transition pressure, large amounts (>3 mmol g-1) of ultrahigh-purity (99.9999999%, 9N; CO concentration < 1 part per billion) gases can be produced in a single adsorption process, demonstrating the usefulness of this approach for separation applications.
Endowing room-temperature polymorphs with both long-term stability and easy interconvertibility is a big challenge due to the complexity of intermolecular interactions. Herein, we present a chiral hexagonal perovskite (R-3-hydroxy-1-methylpiperidinium)[CdCl3 ] having two room-temperature crystalline forms featuring obviously distinct second-harmonic-generation (SHG) signals with a high switching contrast of ~18â times. The two room-temperature forms could be long-term stable yet easily interconvertible through an irreversible thermal-induced phase transition and a pressure-driven backward transition, by switching hydrogen bonds via collective reorientation of ordered homochiral cations. Based on the essential role of homochiral organic cations in inducing switchable hydrogen bond linkages, this present instance provides good evidence that relatively irregular organic cations could induce more obvious inorganic chain deformations, thus endowing polymorphs with significantly different SHG signals at room temperature.
Hybrid ferroelastics have attracted increasing attention for their potential application as mechanical switches. The sporadically documented anomalous ferroelastic phase transitions, i.e., ferroelasticity that appears at a high-temperature phase rather than a low-temperature phase, are of particular interest but are not well understood at the molecular level. By judiciously choosing a polar and flexible organic cation (Me2NH(CH2)2Br+) with cis-/anti- conformations as an A-site component, we obtained two new polar hybrid ferroelastics, A2[MBr6] (M = Te for 1 and Sn for 2). These materials undergo distinct thermal-induced ferroelastic phase transitions. The larger [TeBr6]2- anions anchor the adjacent organic cations well and essentially endow 1 with a conventional ferroelastic transition (P21 â Pm21n) arising from a common order-disorder transition of organic cations without conformational changes. Moreover, the smaller [SnBr6]2- anions can interact with the adjacent organic cations in energetically similar sets of intermolecular interactions, enabling 2 to undergo an anomalous ferroelastic phase transition (P212121 â P21) arising from an unusual cis-/anti-conformational reversal of organic cations. These two instances demonstrate the importance of the delicate balance of intermolecular interactions for inducing anomalous ferroelastic phase transitions. The findings here provide important insights for seeking new multifunctional ferroelastic materials.
Seeking good correspondences between two images is a fundamental and challenging problem in the remote sensing (RS) community, and it is a critical prerequisite in a wide range of feature-based visual tasks. In this article, we propose a flexible and general deep state learning network for both rigid and nonrigid feature matching, which provides a mechanism to change the state of matches into latent canonical forms, thereby weakening the degree of randomness in matching patterns. Different from the current conventional strategies (i.e., imposing a global geometric constraint or designing additional handcrafted descriptor), the proposed StateNet is designed to perform alternating two steps: 1) recalibrates matchwise feature responses in the spatial domain and 2) leverages the spatially local correlation across two sets of feature points for transformation update. For this purpose, our network contains two novel operations: adaptive dual-aggregation convolution (ADAConv) and point rendering layer (PRL). These two operations are differentiable, so our network can be inserted into the existing classification architecture to reduce the cost of establishing reliable correspondences. To demonstrate the robustness and universality of our approach, extensive experiments on various real image pairs for feature matching are conducted. Experiments reveal the superiority of our StateNet significantly over the state-of-the-art alternatives.
Algorithms , Neural Networks, Computer , Image Processing, Computer-Assisted/methods , Remote Sensing Technology
Epidemics caused by pathogens in recent years have created an urgent need for energetic biocidal agents with the capacity of detonation and releasing bactericides. Herein we present a new type of energetic biocidal agents based on a series of iodine-rich molecular perovskites, (H2dabco)M(IO4)3 (dabco = 1,4-diazabicyclo[2.2.2]octane, M = Na+/K+/Rb+/NH4 + for DAI-1/2/3/4) and (H2dabco)Na(H4IO6)3 (DAI-X1). These compounds possess a cubic perovskite structure, and notably have not only high iodine contents (49-54 wt%), but also high performance in detonation velocity (6.331-6.558 km s-1) and detonation pressure (30.69-30.88 GPa). In particular, DAI-4 has a very high iodine content of 54.0 wt% and simultaneously an exceptional detonation velocity up to 6.558 km s-1. As disclosed by laser scanning confocal microscopy observation and a standard micro-broth dilution method, the detonation products of DAI-4 exhibit a broad-spectrum bactericidal effect against bacteria (E. coli, S. aureus, and P. aeruginosa). The advantages of easy scale-up synthesis, low cost, high detonation performance, and high iodine contents enable these periodate-based molecular perovskites to be highly promising candidates for energetic biocidal agents. Electronic Supplementary Material: Supplementary material is available in the online version of this article at 10.1007/s40843-022-2257-6.
Molecular-based ferroic phase-transition materials have attracted increasing attention in the past decades due to their promising potential as sensors, switches, and memory. One of the long-term challenges in the development of molecular-based ferroic materials is determining how to promote the ferroic phase-transition temperature (T c). Herein, we present two new hexagonal molecular perovskites, (nortropinonium)[CdCl3] (1) and (nortropinium)[CdCl3] (2), to demonstrate a simple design principle for obtaining ultrahigh-T c ferroelastic phase transitions. They consist of same host inorganic chains but subtly different guest organic cations featuring a rigid carbonyl and a flexible hydroxyl group in 1 and 2, respectively. With stronger hydrogen bonds involving the carbonyl but a relatively lower decomposition temperature (T d, 480 K), 1 does not exhibit a crystalline phase transition before its decomposition. The hydroxyl group subtly changes the balance of intermolecular interactions in 2via reducing the attractive hydrogen bonds but increasing the repulsive interactions between adjacent organic cations, which finally endows 2 with an enhanced thermal stability (T d = 570 K) and three structural phase transitions, including two ferroelastic phase transitions at ultrahigh T c values of 463 K and 495 K, respectively. This finding provides important clues to judiciously tuning the intermolecular interactions in hybrid crystals for developing high-T c ferroic materials.
Covalent organic frameworks (COFs), as a novel crystalline porous adsorbent, have been attracting significant attention for their synthesis and application exploration due to the advantages of designability, stability, and functionalization. Herein, through increasing the concentration of the acid catalyst, a facile solution-refluxing synthesis method was developed for the preparation of a three-dimensional dynamic COF material, COF-300, with high yields (>90%) and high space−time yields (>28 kg m−3 day−1). This synthesis method not only permits gram-scale synthesis, but also yields products that well maintain porosity and unique guest-dependent dynamic behavior. Moreover, the catalytic activity of COF-300 as a metal-free photocatalyst was explored for the first time. Under 365 nm ultra-violet light irradiation, COF-300 can effectively catalyze the dye degradation (>99%) in wastewater with good recyclability. By adding magnetic Fe3O4 nanoparticles into the solution-refluxing synthesis of COF-300, Fe3O4/COF-300 nanocomposites can be obtained and used as magnetically recyclable photocatalysts, demonstrating the superiority of this facile synthesis procedure. Our study provides new insights for the preparation of COF materials and a constructive exploration for their water treatment application.
The study of the nuclear reaction rate distribution in the MOX core of China Experimental Fast Reactor is a necessary condition for obtaining the operating license. From the characteristics of core structure, both the principle of experimental and experimental system are studies, emphasized on the scheme design such as the size of foils, the irradiation power and counting rate. Firstly, based on the Monte Carlo Codeï¼MCNPï¼and NJOY codes, the distribution of reaction rates is obtained. MCNP is a general-purpose Monte Carlo N-Particle code that can be used for neutron, photon, electron, and so on. The NJOY nuclear data processing system is a comprehensive computer code package for producing to cross sections and related nuclear parameters from ENDF/B VIII.0 evaluated nuclear data. Secondly, the activity measurement of foils was obtained by HPGE, then the reaction rate is determined by data processing. It is worth mentioning that the experimental reactivity introduction has a negligible effect on the reactor core by MCNP. Finally, a set of activation experiment scheme that is suitable for CEFR MOX core is formed, which serves as a guide to carry out activation method experiments.
China has committed to reaching carbon peak before 2030. To realize the carbon peak goal, financial development plays an essential role in developing a green economy. Based on the panel data of 30 provinces in China from 2006 to 2019, this paper explores the impact of financial development on carbon intensity both theoretically and empirically. A financial development index system is constructed and computed using the entropy method. A spatial lag panel data model is employed to empirically test the interaction effect of financial development on carbon intensity. Moreover, the mediating effects of industrial upgrading and technological innovation are further investigated. The results show that: first, carbon intensity generates strong spatial spillover effects between provinces in China. Second, financial development significantly reduces carbon intensity, and is most pronounced in central China, followed by western and eastern China. Third, industrial upgrading and technological innovation are important channels to assist financial development in cutting down carbon intensity, and both produce positive spatial spillover effects. These findings suggest that inter-regional cooperation and coordination on financial development, industrial upgrading, and technological innovation are conducive to achieving low-carbon development targets. This research not only has practical significance to China, but also provides global reference value to other countries.
Carbon , Inventions , Carbon/analysis , Carbon Dioxide/analysis , China , Economic Development , Industry
BACKGROUND: Dental cusp angulation provides valuable insights into chewing efficiency and prosthesis safety. Artificial intelligence-enabled computing of cusp angles has potential important value, but there is currently no reliable digital measurement method as a cornerstone. OBJECTIVES: To establish a digital method for measuring cusp angles and investigate inter-rater and intra-rater reliabilities. METHODS: Two cusp angles (angles α and ß) of the first molar were measured on 21 plaster casts using a goniometer and on their corresponding digital models using PicPick software after scanning with a CEREC Bluecam three-dimensional (3D) intraoral scanner. Means ± standard deviations, as well as intraclass correlation coefficients (ICCs) and Pearson's correlation coefficients (PCCs) were calculated, and repeated measures analysis of variance was performed. The Bland-Altman method was used to calculate the difference and mean degree values of two measurement methods from both examiners, and the Bland-Altman diagram was drawn using MedCalc software. RESULTS: When the examiner was experienced, angle α was 139.19° ± 13.86°, angle ß was 19.25° ± 6.86°, and a very strong positive correlation between the two methods was found (r > 0.9; p < 0.001). No significant difference between the two methods was found using the repeated measures analysis of variance (p > 0.05). The Bland-Altman diagram showed that the two methods were highly consistent. For inter-rater assessments, the ICC and PCC values of the cusp angulation using the digital method were all higher than the corresponding values measured on traditional casts. For intra-rater assessments, the ICC values of cusp angulation using the digital method were higher than the corresponding values measured on traditional plaster casts for both examiners. However, repeated measurements of the angle ß of the inter-examiners revealed significant differences (p < 0.05) for both methods. CONCLUSIONS: Cusp angulation using 3D digital models is a clinical option and appears to improve the reliability of cusp angulation compared with measuring plaster casts using a goniometer. This variability was still evident when measuring small cusp angles using the digital model for inexperienced examiners.
Imaging, Three-Dimensional , Models, Dental , Reproducibility of Results , Imaging, Three-Dimensional/methods , Artificial Intelligence , Software
Building contour extraction from high-resolution remote sensing images is a basic task for the reasonable planning of regional construction. Recently, building segmentation methods based on the U-Net network have become popular as they largely improve the segmentation accuracy by applying 'skip connection' to combine high-level and low-level feature information more effectively. Meanwhile, researchers have demonstrated that introducing an attention mechanism into U-Net can enhance local feature expression and improve the performance of building extraction in remote sensing images. In this paper, we intend to explore the effectiveness of the primeval attention gate module and propose the novel Attention Gate Module (AG) based on adjusting the position of 'Resampler' in an attention gate to Sigmoid function for a building extraction task, and a novel Attention Gates U network (AGs-Unet) is further proposed based on AG, which can automatically learn different forms of building structures in high-resolution remote sensing images and realize efficient extraction of building contour. AGs-Unet integrates attention gates with a single U-Net network, in which a series of attention gate modules are added into the 'skip connection' for suppressing the irrelevant and noisy feature responses in the input image to highlight the dominant features of the buildings in the image. AGs-Unet improves the feature selection of the attention map to enhance the ability of feature learning, as well as paying attention to the feature information of small-scale buildings. We conducted the experiments on the WHU building dataset and the INRIA Aerial Image Labeling dataset, in which the proposed AGs-Unet model is compared with several classic models (such as FCN8s, SegNet, U-Net, and DANet) and two state-of-the-art models (such as PISANet, and ARC-Net). The extraction accuracy of each model is evaluated by using three evaluation indexes, namely, overall accuracy, precision, and intersection over union. Experimental results show that the proposed AGs-Unet model can improve the quality of building extraction from high-resolution remote sensing images effectively in terms of prediction performance and result accuracy.
Image Processing, Computer-Assisted , Neural Networks, Computer , Remote Sensing Technology
Feature matching, which refers to identifying and then corresponding the same or similar visual pattern from two or more images, is a key technique in any image processing task that requires establishing good correspondences between images. Given potential correspondences (matches) in two scenes, a novel whole-part deep learning framework, termed as Context Structure Representation Network (CSR-Net), is designed to infer the probabilities of arbitrary correspondences being inliers. Traditional approaches commonly build the local relation between correspondences by manually engineered criteria. Different from existing attempts, the main idea of our work is to learn explicitly neighborhood structure of each correspondence, allowing us to formulate the matching problem into a dynamic local structure consensus evaluation in an end-to-end fashion. For this purpose, we propose a permutation-invariant STructure Representation (STR) learning module, which can easily merge different types of networks into a unified architecture to deal with sparse matches directly. By the collaborative use of STR, we introduce a Context-Aware Attention (CAA) mechanism to adaptively re-calibrate structure features via a rotation-invariant context aware encoding and simple feature gating, thus arising the ability of fine-grained patterns recognition. Moreover, to further weaken the cost of establishing reliable correspondences, the CSR-Net is formulated as whole-part consensus learning, where the aim of whole level is compensating rigid transformations. In order to demonstrate our CSR-Net can effectively boost the baselines, we intensively experiment on image matching and other visual tasks. The results of the experiment confirm that the matching performances of CSR-Net have significantly improved over nine state-of-the-art competitors.
ABX3-type molecular perovskites provide an important platform to tune phase transitions, via judiciously choosing A-, B-, and X-site components, to approach advanced functional materials for applications. Although tetrafluoroborate can act as X-site component to assemble ten instances of ABX3 molecular perovskites, only two of them possess hexagonal perovskite structures. Herein, we report two tetrafluoroborate-based hexagonal molecular perovskites, A[Na(BF4)3], by judiciously choosing two different A-site cations: 1-methyl-1,4-diazabicyclo[2.2.2]octane-1,4-diium (Hmdabco2+) for 1 and 1-methylpiperazine-1,4-diium (H2mpz2+) for 2. They have high-temperature phases in the same space group (P63/mmc) revealing highly disordered A-site cations. Upon cooling, 1 undergoes two-step P63/mmc â P3Ì c1 â P21/n transitions at 344 and 338 K, respectively, including a ferroelastic one (3Ì mF2/m) accompanied by a spontaneous strain of 0.013. In contrast, the smaller H2mpz2+ cation with more adoptable conformations induces a one-step sharp P63/mmc â P21/c ferroelastic transition (6/mmmF2/m(s)) at 418 K in 2, leading to more significant symmetry breaking and a considerable spontaneous strain of 0.129. This study provides important clues to modulate structural phase transitions by tuning diverse components for the multicomponent dense hybrid crystals.
Switching materials in channels of nonlinear optics (NLOs) are of particular interest in NLO material science. Numerous crystalline NLO switches based on structural phase transition have emerged, but most of them reveal a single-step switch between two different second-harmonic-generation (SHG) states, and only very rare cases involve three or more SHG states. Herein, we report a new organic-inorganic hybrid salt, (Me3 NNH2 )2 [CdI4 ], which is an unprecedented case of a reversible three-step NLO switch between SHG-silent, -medium, -low, and -high states, with high contrasts of 25.5/4.3/9.2 in a temperature range of 213-303â K. By using the combined techniques of variable-temperature X-ray single-crystal structural analyses, dielectric constants, solid-state 13 C nuclear magnetic resonance spectroscopy, and Hirshfeld surface analyses, we disclose that this four-state switchable SHG behavior is highly associated with the stepwise-changed molecular dynamics of the polar organic cations. This finding demonstrates well the complexity of molecular dynamics in simple hybrid salts and their potential in designing new advanced multistep switching materials.
Stimuli-responsive materials that can be reversibly switched by light are of immense interest. Among them, photo-responsive spin crossover (SCO) complexes have great promises to combine the photoactive inputs with multifaceted outputs into switchable materials and devices. However, the reversible control the spin-state change by photochromic guests is still challenging. Herein, we report an unprecedented guest-driven light-induced spin change (GD-LISC) in a Hofmann-type metal-organic framework (MOF), [Fe(bpn){Ag(CN)2 }2 ]â azobenzene. (1, bpn=1,4-bis(4-pyridyl)naphthalene). The reversible trans-cis photoisomerization of azobenzene guest upon UV/Vis irradiation in the solid-state results in the remarkable magnetic changes in a wide temperature range of 10-180â K. This finding not only establishes a new switching mechanism for SCO complexes, but also paves the way toward the development of new generation of photo-responsive magnetic materials.
