Explore the vast range of titles available.

Covalent organic framework (COF) materials have been difficult to characterize structurally and to exploit because they tend to form powders or amorphous materials. Ma et al. studied a variety of three-dimensional COFs based on imine linkages (see the Perspective by Navarro). They found that the addition of aniline inhibited nucleation and allowed the growth of crystals large enough for single-crystal x-ray diffraction studies. Evans et al. describe a two-step process in which nanoscale seeds of boronate ester–linked two-dimensional COFs can be grown into micrometer-scale single crystals by using a solvent that suppresses the nucleation of additional nanoparticles. Transient absorption spectroscopy revealed superior charge transport in these crystallites compared with that observed in conventional powders. Science , this issue p. 48 , p. 52 ; see also p. 35 The addition of aniline enables the growth of single crystals of imine-based covalent organic framework materials. The crystallization problem is an outstanding challenge in the chemistry of porous covalent organic frameworks (COFs). Their structural characterization has been limited to modeling and solutions based on powder x-ray or electron diffraction data. Single crystals of COFs amenable to x-ray diffraction characterization have not been reported. Here, we developed a general procedure to grow large single crystals of three-dimensional imine-based COFs (COF-300, hydrated form of COF-300, COF-303, LZU-79, and LZU-111). The high quality of the crystals allowed collection of single-crystal x-ray diffraction data of up to 0.83-angstrom resolution, leading to unambiguous solution and precise anisotropic refinement. Characteristics such as degree of interpenetration, arrangement of water guests, the reversed imine connectivity, linker disorder, and uncommon topology were deciphered with atomic precision—aspects impossible to determine without single crystals.

The flexible thermoelectric technique, which can convert heat from the human body to electricity via the Seebeck effect, is expected to provide a peerless solution for the power supply of wearables. The recent discovery of ductile semiconductors has opened a new avenue for flexible thermoelectric technology, but their power factor and figure-of-merit values are still much lower than those of classic thermoelectric materials. Herein, we demonstrate the presence of morphotropic phase boundary in Ag 2 Se-Ag 2 S pseudobinary compounds. The morphotropic phase boundary can be freely tuned by adjusting the material thermal treatment processes. High-performance ductile thermoelectric materials with excellent power factor (22 μWcm −1  K −2 ) and figure-of-merit (0.61) values are realized near the morphotropic phase boundary at 300 K. These materials perform better than all existing ductile inorganic semiconductors and organic materials. Furthermore, the in-plane flexible thermoelectric device based on these high-performance thermoelectric materials demonstrates a normalized maximum power density reaching 0.26 Wm −1 under a temperature gradient of 20 K, which is at least two orders of magnitude higher than those of flexible organic thermoelectric devices. This work can greatly accelerate the development of flexible thermoelectric technology. Power factor and figure-of-merit values are normally low in flexible thermoelectric materials. Here, the authors fabricate high-performance ductile thermoelectric materials with high power factor and figure-of-merit values near the morphotropic phase boundary in Ag 2 Se-Ag 2 S pseudobinary compound.

Single-crystalline high- κ dielectric materials are desired for the development of future two-dimensional (2D) electronic devices. However, curent 2D gate insulators still face challenges, such as insufficient dielectric constant and difficult to obtain free-standing and transferrable ultrathin films. Here, we demonstrate that ultrathin Bi 2 SiO 5 crystals grown by chemical vapor deposition (CVD) can serve as excellent gate dielectric layers for 2D semiconductors, showing a high dielectric constant (>30) and large band gap (~3.8 eV). Unlike other 2D insulators synthesized via in-plane CVD on substrates, vertically grown Bi 2 SiO 5 can be easily transferred onto other substrates by polymer-free mechanical pressing, which greatly facilitates its ideal van der Waals integration with few-layer MoS 2 as high- κ dielectrics and screening layers. The Bi 2 SiO 5 gated MoS 2 field-effect transistors exhibit an ignorable hysteresis (~3 mV) and low drain induced barrier lowering (~5 mV/V). Our work suggests vertically grown Bi 2 SiO 5 nanoflakes as promising candidates to improve the performance of 2D electronic devices. Crystalline high-κ dielectric materials are desired for the development of future 2D electronic devices. Here, the authors report the in-plane and out-of-plane chemical vapor deposition growth of ultrathin Bi 2 SiO 5 crystals with dielectric constant >30 and a band gap of ~3.8 eV, showing their effective application as gate dielectric layers of MoS 2 transistors.

The high-dimensional integral–differential nature of the neutron transport equation and the complexity of nuclear reactors result in high computational costs. A set of reduced-order modeling frameworks based on Proper Orthogonal Decomposition (POD) is developed to improve the computational efficiency for neutron diffusion calculations while maintaining accuracy, especially for small samples. For modal coefficient calculations, three methods—Galerkin, radial basis function (RBF), and Deep Neural Network (DNN)—are introduced and analyzed for molten salt reactors. The results show that all three reduced-order models achieve sufficient accuracy, with neutron flux L2 errors below 1% and delayed neutron precursor (DNP) L2 errors below 2.4%, while the acceleration ratios exceed 800. Among these, the POD–Galerkin model demonstrates superior performance, achieving average L2 errors of less than 0.00658% for neutron flux and 1.01% for DNP concentration, with an acceleration ratio of approximately 1800 and excellent extrapolation ability. The POD–Galerkin reduced-order model significantly enhances the computational efficiency for solving neutron multi-group diffusion equations and DNP conservation equations in molten salt reactors while preserving the solution accuracy, making it ideal for a liquid fuel molten salt reactor in the case of small samples.

The discovery of iron-based superconductors (FeSCs), with the highest transition temperature ( T c ) up to 55 K, has attracted worldwide research efforts over the past ten years. So far, all these FeSCs structurally adopt FeSe-type layers with a square iron lattice and superconductivity can be generated by either chemical doping or external pressure. Herein, we report the observation of superconductivity in an iron-based honeycomb lattice via pressure-driven spin-crossover. Under compression, the layered FeP X 3 ( X  = S, Se) simultaneously undergo large in-plane lattice collapses, abrupt spin-crossovers, and insulator-metal transitions. Superconductivity emerges in FePSe 3 along with the structural transition and vanishing of magnetic moment with a starting T c  ~ 2.5 K at 9.0 GPa and the maximum T c  ~ 5.5 K around 30 GPa. The discovery of superconductivity in iron-based honeycomb lattice provides a demonstration for the pursuit of transition-metal-based superconductors via pressure-driven spin-crossover. Up to now, all iron-based high- T c superconductors contain a square iron lattice. Here, Wang et al. report the observation of superconductivity in an iron honeycomb lattice accompanied with pressure-driven spin-crossover, in-plane lattice collapse and insulator-metal transition.

Objectives To summarise the CT findings of gastric poorly cohesive carcinoma (PCC) in the 40 s late arterial phase and differentiate it from tubular adenocarcinoma (TAC) using an integrative nomogram. Methods A total of 241 patients including 59 PCCs, 109 TACs, and 73 other type gastric cancers were enrolled. Thirteen CT morphological characteristics of each lesion in the late arterial phase were evaluated. In addition, CT value–related parameters were extracted from ROIs encompassing the area of greatest enhancement on four-phase CT images. Nomograms based on regression models were built to discriminate PCCs from TACs and from non-PCCs. ROC curve analysis was performed to assess the diagnostic efficiency. Results Six morphological characteristics, 10 CT value–related parameters, and the enhanced curve types differed significantly among the above three groups in the primary cohort (all p  < 0.05). The paired comparison revealed that 10 CT value–related parameters differed significantly between PCCs and TACs (all p  < 0.05). The AUC of the nomogram based on the multivariate model for discriminating PCCs from TACs was 0.954, which was confirmed in the validation cohort (AUC = 0.895). The AUC of another nomogram for discriminating PCCs from non-PCCs was 0.938, which was confirmed in the validation cohort (AUC = 0.880). Conclusions In the 40 s late arterial phase, the morphological characteristics and CT value–related parameters were significantly different among PCCs, TACs, and other types. PCCs were prone to manifest mucosal line interruption, diffuse thickening, infiltrative growth, and slow-rising enhanced curve (Type A). Furthermore, multivariate models were useful in discriminating PCCs from TACs and other types. Key Points • Multiple morphological characteristics and CT value–related parameters differed significantly between gastric PCCs and TACs in the 40 s late arterial phase. • The nomogram integrating morphological characteristics and CT value–related parameters in the 40 s late arterial phase had favourable performance in discriminating PCCs from TACs. • More useful information can be derived from 40 s late arterial phase CT images; thus, a more accurate evaluation can be made in clinical practice.

Background To investigate bone, adipose, and muscle-related body compositions changes in gastric cancers (GCs) 12 months after gastrectomy utilizing an artificial intelligence (AI) based segmentation tool and to conduct subgroup analyses based on clinicopathological characteristics. Methods This retrospective study included 146 GCs who underwent gastrectomy. Body compositions of GCs at baseline and 12 months after surgery were automatically measured utilizing the AI-based segmentation tool. The differences in body compositions at baseline and 12 months after surgery were assessed. Subgroup analyses of body composition changes stratified by clinicopathological characteristics were conducted. Benjamini-Hochberg false discovery rate (FDR) correction was utilized for all subgroup analyses. Results All body composition parameters, including bone mineral density (BMD), adipose tissue, and muscle, decreased significantly 12 months after surgery (all p  < 0.001). The greatest losses were observed in adipose-related compositions. The proportions of sarcopenia (from 37.7% to 55.5%) and osteoporosis (from 13.7% to 26.7%) showed significant increases. Subcutaneous and visceral adipose tissues, abdominal wall muscle, and skeletal muscle losses indicated significant differences in gender and body mass index (BMI) subgroups (all FDR p  < 0.05). Adipose- and muscle-related losses differed significantly in GCs underwent different types of gastrectomy (all FDR p  < 0.05), while BMD loss showed no significant difference. Subgroup analyses based on pathological stages showed no significant difference for body composition changes. Conclusions BMD, adipose-related, and muscle-related body compositions showed significant losses over 12 months in GCs underwent gastrectomy. The greatest losses were observed in adipose-related compositions. The proportions of sarcopenia and osteoporosis showed significant increases. Gender, baseline BMI, and gastrectomy differences affected adipose- and muscle-related body composition losses but showed no significant effect on BMD.

Background This study investigated the predictive ability of a transformer model utilizing intratumoral, peritumoral, and habitat features derived from pretreatment 18 F-FDG PET imaging to assess overall survival (OS) in patients with cervical cancer. Methods A retrospective analysis was performed using pretreatment PET data from 107 patients with cervical cancer across two medical institutions. The k-means unsupervised clustering algorithm categorized the tumor and its 4 mm peritumoral region into four distinct habitat subregions. Radiomic features were extracted from the intratumoral, peritumoral, and each habitat subregion to construct intratumoral, peritumoral, habitat, and combined transformer models. Model performance was evaluated using the area under the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. Results The habitat subregion 1 model demonstrated the highest performance. Among individual models, the habitat transformer model achieved the strongest results, with an external validation set area under the curve (AUC) of 0.778 (95% CI: 0.612–0.944), surpassing the intratumoral transformer model (AUC 0.714, 95% CI: 0.521–0.907) and the peritumoral transformer model (AUC 0.707, 95% CI: 0.517–0.896). The combined model further enhanced predictive accuracy, attaining a validation set AUC of 0.823 (95% CI: 0.677–0.970), and exhibited superior calibration and clinical applicability. Conclusion This study highlights the efficacy of the transformer model based on 18 F-FDG PET habitat features in predicting cervical cancer prognosis. The combined model, integrating intratumoral, peritumoral, and habitat features, significantly improves predictive performance and provides valuable insights for personalized treatment planning.

Objectives To explore the application of computed tomography (CT) texture analysis in predicting histopathological features of gastric cancers. Methods Preoperative contrast-enhanced CT images and postoperative histopathological features of 107 patients (82 men, 25 women) with gastric cancers were retrospectively reviewed. CT texture analysis generated: (1) mean attenuation, (2) standard deviation, (3) max frequency, (4) mode, (5) minimum attenuation, (6) maximum attenuation, (7) the fifth, 10th, 25th, 50th, 75th and 90th percentiles, and (8) entropy. Correlations between CT texture parameters and histopathological features were analysed. Results Mean attenuation, maximum attenuation, all percentiles and mode derived from portal venous CT images correlated significantly with differentiation degree and Lauren classification of gastric cancers (r, −0.231 ~ −0.324, 0.228 ~ 0.321, respectively). Standard deviation and entropy derived from arterial CT images also correlated significantly with Lauren classification of gastric cancers (r = −0.265, −0.222, respectively). In arterial phase analysis, standard deviation and entropy were significantly lower in gastric cancers with than those without vascular invasion; however, minimum attenuation was significantly higher in gastric cancers with than those without vascular invasion. Conclusion CT texture analysis held great potential in predicting differentiation degree, Lauren classification and vascular invasion status of gastric cancers. Key Points • CT texture analysis is noninvasive and effective for gastric cancer . • Portal venous CT images correlated significantly with differentiation degree and Lauren classification . • Standard deviation, entropy and minimum attenuation in arterial phase reflect vascular invasion .

Approximately 15% of patients with colorectal cancer (CRC) exhibit a distinct molecular phenotype known as microsatellite instability (MSI). Accurate and non-invasive prediction of MSI status is crucial for cost savings and guiding clinical treatment strategies. The retrospective study enrolled 307 CRC patients between January 2020 and October 2022. Preoperative images of computed tomography and postoperative status of MSI information were available for analysis. The stratified fivefold cross-validation was used to avoid sample bias in grouping. Feature extraction and model construction were performed as follows: first, inter-/intra-correlation coefficients and the least absolute shrinkage and selection operator algorithm were used to identify the most predictive feature subset. Subsequently, multiple discriminant models were constructed to explore and optimize the combination of six feature preprocessors (Box-Cox, Yeo-Johnson, Max-Abs, Min–Max, Z-score, and Quantile) and three classifiers (logistic regression, support vector machine, and random forest). Selecting the one with the highest average value of the area under the curve (AUC) in the test set as the radiomics model, and the clinical screening model and combined model were also established using the same processing steps as the radiomics model. Finally, the performances of the three models were evaluated and analyzed using decision and correction curves.We observed that the logistic regression model based on the quantile preprocessor had the highest average AUC value in the discriminant models. Additionally, tumor location, the clinical of N stage, and hypertension were identified as independent clinical predictors of MSI status. In the test set, the clinical screening model demonstrated good predictive performance, with the average AUC of 0.762 (95% confidence interval, 0.635–0.890). Furthermore, the combined model showed excellent predictive performance (AUC, 0.958; accuracy, 0.899; sensitivity, 0.929) and favorable clinical applicability and correction effects. The logistic regression model based on the quantile preprocessor exhibited excellent performance and repeatability, which may further reduce the variability of input data and improve the model performance for predicting MSI status in CRC.