Modelling human blastocysts by reprogramming fibroblasts into iBlastoids.

Human pluripotent and trophoblast stem cells have been essential alternatives to blastocysts for understanding early human development1-4. However, these simple culture systems lack the complexity to adequately model the spatiotemporal cellular and molecular dynamics that occur during early embryonic development. Here we describe the reprogramming of fibroblasts into in vitro three-dimensional models of the human blastocyst, termed iBlastoids. Characterization of iBlastoids shows that they model the overall architecture of blastocysts, presenting an inner cell mass-like structure, with epiblast- and primitive endoderm-like cells, a blastocoel-like cavity and a trophectoderm-like outer layer of cells. Single-cell transcriptomics further confirmed the presence of epiblast-, primitive endoderm-, and trophectoderm-like cells. Moreover, iBlastoids can give rise to pluripotent and trophoblast stem cells and are capable of modelling, in vitro, several aspects of the early stage of implantation. In summary, we have developed a scalable and tractable system to model human blastocyst biology; we envision that this will facilitate the study of early human development and the effects of gene mutations and toxins during early embryogenesis, as well as aiding in the development of new therapies associated with in vitro fertilization.

High-Strength Polycrystalline Covalent Organic Framework with Abnormal Thermal Transport Insensitive to Grain Boundary.

Grain boundaries (GBs) in two-dimensional (2D) covalent organic frameworks (COFs) unavoidably form during the fabrication process, playing pivotal roles in the physical characteristics of COFs. Herein, molecular dynamics simulations were employed to elucidate the fracture failure and thermal transport mechanisms of polycrystalline COFs (p-COFs). The results revealed that the tilt angle of GBs significantly influences out-of-plane wrinkles and residual stress in monolayer p-COFs. The tensile strength of p-COFs can be enhanced and weakened with the tilt angle, which exhibits an inverse relationship with the defect density. The crack always originates from weaker heptagon rings during uniaxial tension. Notably, the thermal transport in p-COFs is insensitive to the GBs due to the variation of minor polymer chain length at defects, which is abnormal for other 2D crystalline materials. This study contributes insights into the impact of GBs in p-COFs and offers theoretical guidance for structural design and practical applications of advanced COFs.

Dietary Fiber and Microbiota Metabolite Receptors Enhance Cognition and Alleviate Disease in the 5xFAD Mouse Model of Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder with poorly understood etiology. AD has several similarities with other "Western lifestyle" inflammatory diseases, where the gut microbiome and immune pathways have been associated. Previously, we and others have noted the involvement of metabolite-sensing GPCRs and their ligands, short-chain fatty acids (SCFAs), in protection of numerous Western diseases in mouse models, such as Type I diabetes and hypertension. Depletion of GPR43, GPR41, or GPR109A accelerates disease, whereas high SCFA yielding diets protect in mouse models. Here, we extended the concept that metabolite-sensing receptors and SCFAs may be a more common protective mechanism against Western diseases by studying their role in AD pathogenesis in the 5xFAD mouse model. Both male and female mice were included. Depletion of GPR41 and GPR43 accelerated cognitive decline and impaired adult hippocampal neurogenesis in 5xFAD and WT mice. Lack of fiber/SCFAs accelerated a memory deficit, whereas diets supplemented with high acetate and butyrate (HAMSAB) delayed cognitive decline in 5xFAD mice. Fiber intake impacted on microglial morphology in WT mice and microglial clustering phenotype in 5xFAD mice. Lack of fiber impaired adult hippocampal neurogenesis in both W and AD mice. Finally, maternal dietary fiber intake significantly affects offspring's cognitive functions in 5xFAD mice and microglial transcriptome in both WT and 5xFAD mice, suggesting that SCFAs may exert their effect during pregnancy and lactation. Together, metabolite-sensing GPCRs and SCFAs are essential for protection against AD, and reveal a new strategy for disease prevention.Significance Statement Alzheimer's disease (AD) is one of the most common neurodegenerative diseases; currently, there is no cure for AD. In our study, short-chain fatty acids and metabolite receptors play an important role in cognitive function and pathology in AD mouse model as well as in WT mice. SCFAs also impact on microglia transcriptome, and immune cell recruitment. Out study indicates the potential of specialized diets (supplemented with high acetate and butyrate) releasing high amounts of SCFAs to protect against disease.

Spatially and Temporally Programmable Transparency Evolutions in Hydrogels Enabled by Metal Coordination toward Transient Anticounterfeiting.

Hydrogels have emerged as promising candidates for anticounterfeiting materials, owing to their unique stimulus-responsive capabilities. To improve the security of encrypted information, efforts are devoted to constructing transient anticounterfeiting hydrogels with a dynamic information display. However, current studies to design such hydrogel materials inevitably include sophisticated chemistry, complex preparation processes, and particular experimental setups. Herein, a facile strategy is proposed to realize the transient anticounterfeiting by constructing bivalent metal (M2+)-coordination complexes in poly(acrylic acid) gels, where the cloud temperature (Tc) of the gels can be feasibly tuned by M2+ concentration. Therefore, the multi-Tc parts in the gel can be locally programmed by leveraging the spatially selective diffusion of M2+ with different concentrations. With the increase of temperature or the addition of a complexing agent, the transparency of the multi-Tc parts in the gel spontaneously evolves in natural light, enabling the transient information anticounterfeiting process. This work has provided a new strategy and mechanism to fabricate advanced anticounterfeiting hydrogel materials.

Elimination of Intragrain Defect to Enhance the Performance of FAPbI3 Perovskite Solar Cells by Ionic Liquid.

Ionic liquids have been widely used to improve the efficiency and stability of perovskite solar cells (PSCs), and are generally believed to passivate defects on the grain boundaries of perovskites. However, few studies have focused on the relevant effects of ionic liquids on intragrain defects in perovskites which have been shown to be critical for the performance of PSCs. In this work, the effect of ionic liquid 1-hexyl-3-methylimidazolium iodide (HMII) on intragrain defects of formamidinium lead iodide (FAPbI3) perovskite is investigated. Abundant {111}c intragrain planar defects in pure FAPbI3 grains are found to be significantly reduced by the addition of the ionic liquid HMII, shown by using ultra-low-dose selected area electron diffraction. As a result, longer charge carrier lifetimes, higher photoluminescence quantum yield, better charge carrier transport properties, lower Urbach energy, and current-voltage hysteresis are achieved, and the champion power conversion efficiency of 24.09% is demonstrated. These observations suggest that ionic liquids significantly improve device performance resulting from the elimination of {111}c intragrain planar defects.

Effects of prospective motion correction on perivascular spaces at 7T MRI evaluated using motion artifact simulation.

PURPOSE: The effectiveness of prospective motion correction (PMC) is often evaluated by comparing artifacts in images acquired with and without PMC (NoPMC). However, such an approach is not applicable in clinical setting due to unavailability of NoPMC images. We aim to develop a simulation approach for demonstrating the ability of fat-navigator-based PMC in improving perivascular space (PVS) visibility in T2-weighted MRI. METHODS: MRI datasets from two earlier studies were used for motion artifact simulation and evaluating PMC, including T2-weighted NoPMC and PMC images. To simulate motion artifacts, k-space data at motion-perturbed positions were calculated from artifact-free images using nonuniform Fourier transform and misplaced onto the Cartesian grid before inverse Fourier transform. The simulation's ability to reproduce motion-induced blurring, ringing, and ghosting artifacts was evaluated using sharpness at lateral ventricle/white matter boundary, ringing artifact magnitude in the Fourier spectrum, and background noise, respectively. PVS volume fraction in white matter was employed to reflect its visibility. RESULTS: In simulation, sharpness, PVS volume fraction, and background noise exhibited significant negative correlations with motion score. Significant correlations were found in sharpness, ringing artifact magnitude, and PVS volume fraction between simulated and real NoPMC images (p ≤ 0.006). In contrast, such correlations were reduced and nonsignificant between simulated and real PMC images (p ≥ 0.48), suggesting reduction of motion effects with PMC. CONCLUSIONS: The proposed simulation approach is an effective tool to study the effects of motion and PMC on PVS visibility. PMC may reduce the systematic bias of PVS volume fraction caused by motion artifacts.

Derivation and External Validation of Machine Learning-Based Model for Detection of Pancreatic Cancer.

INTRODUCTION: There is currently no widely accepted approach to screening for pancreatic cancer (PC). We aimed to develop and validate a risk prediction model for pancreatic ductal adenocarcinoma (PDAC), the most common form of PC, across 2 health systems using electronic health records. METHODS: This retrospective cohort study consisted of patients aged 50-84 years having at least 1 clinic-based visit over a 10-year study period at Kaiser Permanente Southern California (model training, internal validation) and the Veterans Affairs (VA, external testing). Random survival forests models were built to identify the most relevant predictors from >500 variables and to predict risk of PDAC within 18 months of cohort entry. RESULTS: The Kaiser Permanente Southern California cohort consisted of 1.8 million patients (mean age 61.6) with 1,792 PDAC cases. The 18-month incidence rate of PDAC was 0.77 (95% confidence interval 0.73-0.80)/1,000 person-years. The final main model contained age, abdominal pain, weight change, HbA1c, and alanine transaminase change (c-index: mean = 0.77, SD = 0.02; calibration test: P value 0.4, SD 0.3). The final early detection model comprised the same features as those selected by the main model except for abdominal pain (c-index: 0.77 and SD 0.4; calibration test: P value 0.3 and SD 0.3). The VA testing cohort consisted of 2.7 million patients (mean age 66.1) with an 18-month incidence rate of 1.27 (1.23-1.30)/1,000 person-years. The recalibrated main and early detection models based on VA testing data sets achieved a mean c-index of 0.71 (SD 0.002) and 0.68 (SD 0.003), respectively. DISCUSSION: Using widely available parameters in electronic health records, we developed and externally validated parsimonious machine learning-based models for detection of PC. These models may be suitable for real-time clinical application.

Machine learning versus regression for prediction of sporadic pancreatic cancer.

BACKGROUND/OBJECTIVES: There is currently no widely accepted approach to identify patients at increased risk for sporadic pancreatic cancer (PC). We aimed to compare the performance of two machine-learning models with a regression-based model in predicting pancreatic ductal adenocarcinoma (PDAC), the most common form of PC. METHODS: This retrospective cohort study consisted of patients 50-84 years of age enrolled in either Kaiser Permanente Southern California (KPSC, model training, internal validation) or the Veterans Affairs (VA, external testing) between 2008 and 2017. The performance of random survival forests (RSF) and eXtreme gradient boosting (XGB) models were compared to that of COX proportional hazards regression (COX). Heterogeneity of the three models were assessed. RESULTS: The KPSC and the VA cohorts consisted of 1.8 and 2.7 million patients with 1792 and 4582 incident PDAC cases within 18 months, respectively. Predictors selected into all three models included age, abdominal pain, weight change, and glycated hemoglobin (A1c). Additionally, RSF selected change in alanine transaminase (ALT), whereas the XGB and COX selected the rate of change in ALT. The COX model appeared to have lower AUC (KPSC: 0.737, 95% CI 0.710-0.764; VA: 0.706, 0.699-0.714), compared to those of RSF (KPSC: 0.767, 0.744-0.791; VA: 0.731, 0.724-0.739) and XGB (KPSC: 0.779, 0.755-0.802; VA: 0.742, 0.735-0.750). Among patients with top 5% predicted risk from all three models (N = 29,663), 117 developed PDAC, of which RSF, XGB and COX captured 84 (9 unique), 87 (4 unique), 87 (19 unique) cases, respectively. CONCLUSIONS: The three models complement each other, but each has unique contributions.

Biofilm and Small Colony Variants-An Update on Staphylococcus aureus Strategies toward Drug Resistance.

Recently, the drawbacks arising from the overuse of antibiotics have drawn growing public attention. Among them, drug-resistance (DR) and even multidrug-resistance (MDR) pose significant challenges in clinical practice. As a representative of a DR or MDR pathogen, Staphylococcus aureus can cause diversity of infections related to different organs, and can survive or adapt to the diverse hostile environments by switching into other phenotypes, including biofilm and small colony variants (SCVs), with altered physiologic or metabolic characteristics. In this review, we briefly describe the development of the DR/MDR as well as the classical mechanisms (accumulation of the resistant genes). Moreover, we use multidimensional scaling analysis to evaluate the MDR relevant hotspots in the recent published reports. Furthermore, we mainly focus on the possible non-classical resistance mechanisms triggered by the two important alternative phenotypes of the S. aureus, biofilm and SCVs, which are fundamentally caused by the different global regulation of the S. aureus population, such as the main quorum-sensing (QS) and agr system and its coordinated regulated factors, such as the SarA family proteins and the alternative sigma factor σB (SigB). Both the biofilm and the SCVs are able to escape from the host immune response, and resist the therapeutic effects of antibiotics through the physical or the biological barriers, and become less sensitive to some antibiotics by the dormant state with the limited metabolisms.

Atomically Dispersed Indium-Copper Dual-Metal Active Sites Promoting C-C Coupling for CO2 Photoreduction to Ethanol.

Photoreduction of CO2 to C2+ solar fuel is a promising carbon-neutral technology for renewable energy. This strategy is challenged by its low productivity due to low efficiency in multielectron utilization and slow C-C coupling kinetics. This work reports a dual-metal photocatalyst consisting of atomically dispersed indium and copper anchored on polymeric carbon nitride (InCu/PCN), on which the photoreduction of CO2 delivered an excellent ethanol production rate of 28.5 µmol g-1 h-1 with a high selectivity of 92 %. Coupled experimental investigation and DFT calculations reveal the following mechanisms underpinning the high performance of this catalyst. Essentially, the In-Cu interaction enhances the charge separation by accelerating charge transfer from PCN to the metal sites. Indium also transfers electrons to neighboring copper via Cu-N-In bridges, increasing the electron density of copper active sites. Furthermore, In-Cu dual-metal sites promote the adsorption of *CO intermediates and lower the energy barrier of C-C coupling.

A Novel Transformers Fault Diagnosis Method Based on Probabilistic Neural Network and Bio-Inspired Optimizer.

Since it is difficult for the traditional fault diagnosis method based on dissolved gas analysis (DGA) to meet today's engineering needs in terms of diagnostic accuracy and stability, this paper proposes an artificial intelligence fault diagnosis method based on a probabilistic neural network (PNN) and bio-inspired optimizer. The PNN is used as the basic classifier of the fault diagnosis model, and the bio-inspired optimizer, improved salp swarm algorithm (ISSA), is used to optimize the hidden layer smoothing factor of PNN, which stably improves the classification performance of PNN. Compared with the traditional SSA, the sine cosine algorithm (SCA) and disruption operator are introduced in ISSA, which effectively improves the exploration capability and convergence speed. To verify the engineering applicability of the proposed method, the ISSA-PNN model was developed and tested using sensor data provided by Jiangxi Province Power Supply Company. In addition, the method is compared with machine learning methods such as support vector machine (SVM), back propagation neural network (BPNN), multi-layer perceptron (MLP), and traditional fault diagnosis methods such as the international electrotechnical commission (IEC) ratio method. The results show that the proposed method has a strong learning ability for complex fault data and has advantages in accuracy and robustness compared to other methods.

Etched ZIF-8 as a Filler in Mixed-Matrix Membranes for Enhanced CO2 /N2 Separation.

Zeolite ZIF-8 has been etched with acid to form microporous ZIF-8-E crystals. These were then introduced into a polyethersulfone (PES) membrane matrix to enhance its CO2 /N2 separation performance. Open through pores of size about 100 nm formed in the ZIF-8 crystals allow the ingrowth of polyethersulfone chains, ensuring a reduction in the number of nonselective voids, thereby achieving better interaction between ZIF-8-E and PES. As a result, the CO2 /N2 separation performance of the ZIF-8-E/PES membrane increased significantly, showing a CO2 permeability of 15.7 Barrer and a CO2 /N2 ideal selectivity of 6.5.

Loss of Myh14 Increases Susceptibility to Noise-Induced Hearing Loss in CBA/CaJ Mice.

MYH14 is a member of the myosin family, which has been implicated in many motile processes such as ion-channel gating, organelle translocation, and the cytoskeleton rearrangement. Mutations in MYH14 lead to a DFNA4-type hearing impairment. Further evidence also shows that MYH14 is a candidate noise-induced hearing loss (NIHL) susceptible gene. However, the specific roles of MYH14 in auditory function and NIHL are not fully understood. In the present study, we used CRISPR/Cas9 technology to establish a Myh14 knockout mice line in CBA/CaJ background (now referred to as Myh14-/- mice) and clarify the role of MYH14 in the cochlea and NIHL. We found that Myh14-/- mice did not exhibit significant hearing loss until five months of age. In addition, Myh14-/- mice were more vulnerable to high intensity noise compared to control mice. More significant outer hair cell loss was observed in Myh14-/- mice than in wild type controls after acoustic trauma. Our findings suggest that Myh14 may play a beneficial role in the protection of the cochlea after acoustic overstimulation in CBA/CaJ mice.

[Study on Technological Characteristic of the Proto-Porcelain on Western Zhou Dynasty Excavated from Zhouyuan Site in Shaanxi Province].

In southern region, the proto- porcelain have been found in large number and its excavation sites are relatively concentrated, thus its processing technique and origin have been studied thoroughly. However, as to proto-porcelain in northern region, the unearthed sites are scattered in distribution and its quantity is less. So there is limited analysis as to the technology. Since shaanxi zhouyuan relics unearthed a large number of proto-porcelain, it is necessary to give detailed analysis to them. In order to explore the technological characteristic of proto-porcelain of Western Zhou Dynasty which was excavated from Zhouyuan site in Shaanxi province, in this article three-dimensional video microscopy system was used to observe the microstructure and energy dispersive X-ray Fluorescence Spectrometer was used to test the chemical composition of the proto-porcelain body and glaze. The results of microscopic observation indicated that the proto-porcelain body quality was rough and had many unmelting particles and pores; the glaze layer was uneven and distributes many bubbles. The results of chemical composition in the body showed that the content of Al2O3 was between 11.8%～17.21%, SiO2 is 75%～80.5%, K2O is 3%～7.85%. However the content in the glaze of CaO is between 11.08%～23.94%, P2O5 is 1%～3.18%, MnO is 0.24%～1%; the content of MnO, P2O5, K2O in the glaze had improved greatly more than those in the body. The above results showed that the raw materials of proto-porcelain body may use the chinastone which contains more potassium; and the plant ash should be added in the calcareous glaze; the manufacturing characteristic of the proto-porcelain found in Zhouyuan site was still at the primary stage in Chinese porcelain's history.

A Soft Sensor for Multirate Quality Variables Based on MC-CNN.

In recent years, data-driven soft sensor modeling methods have been widely used in industrial production, chemistry, and biochemical. In industrial processes, the sampling rates of quality variables are always lower than those of process variables. Meanwhile, the sampling rates among quality variables are also different. However, few multi-input multi-output (MIMO) sensors take this temporal factor into consideration. To solve this problem, a deep-learning (DL) model based on a multitemporal channels convolutional neural network (MC-CNN) is proposed. In the MC-CNN, the network consists of two parts: the shared network used to extract the temporal feature and the parallel prediction network used to predict each quality variable. The modified BP algorithm makes the blank values generated at unsampled moments not participate in the backpropagation (BP) process during training. By predicting multiple quality variables of two industrial cases, the effectiveness of the proposed method is verified.

Segmentation of lung nodules based on a refined segmentation network.

BACKGROUND: Accurate segmentation of lung nodules is of great significance for early screening and diagnosis of lung cancer. PURPOSE: However, the heterogeneity of lung nodules and the similarities between them and other lung tissues make it difficult to accurately segment these nodules. As regards the use of deep learning to segment lung nodules, convolutional neural networks would gradually lead to errors accumulating at the network layer due to the presence of multiple upsampling and downsampling layers, resulting in poor segmentation results. METHODS: In this study, we developed a refined segmentation network (RS-Net) for lung nodule segmentation to solve this problem. Accordingly, the proposed RS-Net was first used to locate the core region of the lung nodules and to gradually refine the segmentation results of the core region. In addition, to solve the problem of misdetection of small-sized nodules owing to the imbalance of positive and negative samples, we devised an average dice-loss function computed on nodule level. By calculating the loss of each nodule sample to measure the overall loss, the network can address the misdetection problem of lung nodules with smaller diameters more efficiently. RESULTS: Our method was evaluated based on 1055 lung nodules from Lung Image Database Consortium data and a set of 120 lung nodules collected from Shanghai Chest Hospital for additional validation. The segmentation dice coefficients of RS-Net on these two datasets were 85.90% and 81.13%, respectively. The analysis of the segmentation effect of different properties and sizes of nodules indicates that RS-Net yields a stable segmentation effect. CONCLUSIONS: The results show that the segmentation strategy based on gradual refinement can considerably improve the segmentation of lung nodules.

Cellular and Molecular Roles of Immune Cells in the Gut-Brain Axis in Migraine.

Migraine is a complex and multi-system dysfunction. The realization of its pathophysiology and diagnosis is developing rapidly. Migraine has been linked to gastrointestinal disorders such as irritable bowel syndrome and celiac disease. There is also direct and indirect evidence for a relationship between migraine and the gut-brain axis, but the exact mechanism is not yet explained. Studies have shown that this interaction appears to be influenced by a variety of factors, such as inflammatory mediators, gut microbiota, neuropeptides, and serotonin pathways. Recent studies suggest that immune cells can be the potential tertiary structure between migraine and gut-brain axis. As the hot interdisciplinary subject, the relationship between immunology and gastrointestinal tract is now gradually clear. Inflammatory signals are involved in cellular and molecular responses that link central and peripheral systems. The gastrointestinal symptoms associated with migraine and experiments associated with antibiotics have shown that the intestinal microbiota is abnormal during the attacks. In this review, we focus on the mechanism of migraine and gut-brain axis, and summarize the tertiary structure between immune cells, neural network, and gastrointestinal tract.

Early features of pancreatic cancer on magnetic resonance imaging (MRI): a case-control study.

PURPOSE: Magnetic resonance imaging has been recommended as a primary imaging modality among high-risk individuals undergoing screening for pancreatic cancer. We aimed to delineate potential precursor lesions for pancreatic cancer on MR imaging. METHODS: We conducted a case-control study at Kaiser Permanente Southern California (2008-2018) among patients that developed pancreatic cancer who had pre-diagnostic MRI examinations obtained 2-36 months prior to cancer diagnosis (cases) matched 1:2 by age, gender, race/ethnicity, contrast status and year of scan (controls). Patients with history of acute/chronic pancreatitis or prior pancreatic surgery were excluded. Images underwent blind review with assessment of a priori defined series of parenchymal and ductal features. We performed logistic regression to assess the associations between individual factors and pancreatic cancer. We further assessed the interaction among features as well as performed a sensitivity analysis stratifying based on specific time-windows (2-3 months, 4-12 months, 13-36 months prior to cancer diagnosis). RESULTS: We identified 141 cases (37.9% stage I-II, 2.1% III, 31.4% IV, 28.6% unknown) and 292 matched controls. A solid mass was noted in 24 (17%) of the pre-diagnostic MRI scans. Compared to controls, pre-diagnostic images from cancer cases more frequently exhibited the following ductal findings: main duct dilatation (51.4% vs 14.3%, OR [95% CI]: 7.75 [4.19-15.44], focal pancreatic duct stricture with distal (upstream) dilatation (43.6% vs 5.6%, OR 12.71 [6.02-30.89], irregularity (42.1% vs 6.0%, OR 9.73 [4.91-21.43]), focal pancreatic side branch dilation (13.6% vs1.6%, OR 11.57 [3.38-61.32]) as well as parenchymal features: atrophy (57.9% vs 27.4%, OR 46.4 [2.71-8.28], focal area of signal abnormality (39.3% vs 4.8%, OR 15.69 [6.72-44,78]), all p < 0.001). CONCLUSION: In addition to potential missed lesions, we have identified a series of ductal and parenchymal features on MRI that are associated with increased odds of developing pancreatic cancer.

Ultrasensitive Ionic Conductors with Tunable Resistance Switching Temperature Enabled by Phase Transformation of Polymer Cocrystals.

Phase-transformable ionic conductors (PTICs) show significant prospects for functional applications due to their reversible resistance switching property. However, the representative design principle of PTICs is utilizing the melt-crystallization transition of ionic liquids, and the resistance switching temperatures of such PTICs cannot be tuned as desired. Herein, a new strategy is proposed to design PTICs with on-demand resistance switching temperatures by using the melt-crystallization transition of polymer cocrystal phase, whose melting temperature shows a linear relationship with the polymer compositions. Owing to the melt of polymer cocrystal domains and the tunable migration of ions in the resistance switching region, the obtained PTICs display ultrahigh temperature sensitivity with a superior temperature coefficient of resistance of -8.50% °C-1 around human body temperature, as compared to various ionic conductors previously reported. Therefore, the PTICs can detect tiny temperature variation, allowing for the intelligent applications for overheating warning and heat dissipation. It is believed that this work may inspire future researches on the development of advanced soft electrical devices.

Dynamic Insights into the Growth Mechanisms of 2D Covalent Organic Frameworks on Graphene Surfaces.

Producing high-quality two-dimensional (2D) covalent organic frameworks (COFs) is crucial for industrial applications. However, this remains significantly challenging with current synthetic techniques. A deep understanding of the intermolecular interactions, reaction temperature, and oligomers is essential to facilitate the growth of highly crystalline COF films. Herein, molecular dynamics simulations were employed to explore the growth of 2D COFs from monomer assemblies on graphene. Our results showed that chain growth reactions dominated the COF surface growth and that van der Waals (vdW) interactions were important in enhancing the crystallinity through monomer preorganization. Moreover, appropriately tuning the reaction temperature improved the COF crystallinity and minimized the effects of amorphous oligomers. Additionally, the strength of the interface between the COF and the graphene substrate indicated that the adhesion force was proportional to the crystallinity of the COF. This work reveals the mechanisms for nucleation and growth of COFs on surfaces and provides theoretical guidance for fabricating high-quality 2D polymer-based crystalline nanomaterials.