Design and synthesis of broadband absorption covalent organic framework for efficient artificial photocatalytic amine coupling.

Developing highly active materials that efficiently utilize solar spectra is crucial for photocatalysis, but still remains a challenge. Here, we report a new donor-acceptor (D-A) covalent organic framework (COF) with a wide absorption range from 200 nm to 900 nm (ultraviolet-visible-near infrared light). We find that the thiophene functional group is accurately introduced into the electron acceptor units of TpDPP-Py (TpDPP: 5,5'-(2,5-bis(2-ethylhexyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo [3,4-c]pyrrole-1,4-diyl)bis(thiophene-2-carbaldehyde), Py: 1,3,6,8-tetrakis(4-aminophenyl)pyrene) COFs not only significantly extends its spectral absorption capacity but also endows them with two-photon and three-photon absorption effects, greatly enhancing the utilization rate of sunlight. The selective coupling of benzylamine as the target reactant is used to assess the photocatalytic activity of TpDPP-Py COFs, showing high photocatalytic conversion of 99% and selectivity of 98% in 20 min. Additionally, the TpDPP-Py COFs also exhibit the universality of photocatalytic selective coupling of other imine derivatives with ~100% conversion efficiency. Overall, this work brings a significant strategy for developing COFs with a wide absorption range to enhance photocatalytic activity.

Strain-insensitive viscoelastic perovskite film for intrinsically stretchable neuromorphic vision-adaptive transistors.

Stretchable neuromorphic optoelectronics present tantalizing opportunities for intelligent vision applications that necessitate high spatial resolution and multimodal interaction. Existing neuromorphic devices are either stretchable but not reconcilable with multifunctionality, or discrete but with low-end neurological function and limited flexibility. Herein, we propose a defect-tunable viscoelastic perovskite film that is assembled into strain-insensitive quasi-continuous microsphere morphologies for intrinsically stretchable neuromorphic vision-adaptive transistors. The resulting device achieves trichromatic photoadaptation and a rapid adaptive speed (<150 s) beyond human eyes (3 ~ 30 min) even under 100% mechanical strain. When acted as an artificial synapse, the device can operate at an ultra-low energy consumption (15 aJ) (far below the human brain of 1 ~ 10 fJ) with a high paired-pulse facilitation index of 270% (one of the best figures of merit in stretchable synaptic phototransistors). Furthermore, adaptive optical imaging is achieved by the strain-insensitive perovskite films, accelerating the implementation of next-generation neuromorphic vision systems.

The Transfer Dehydrogenation Method Enables a Family of High Crystalline Benzimidazole-linked Cu (II)-phthalocyanine-based Covalent Organic Frameworks Films.

Heterocycle-linked phthalocyanine-based COFs with close-packed π-π conjugated structures are a kind of material with intrinsic electrical conductivity, and they are considered to be candidates for photoelectrical devices. Previous studies have revealed their applications for energy storage, gas sensors, and field-effect transistors. However, their potential application in photodetector is still not fully studied. The main difficulty is preparing high-quality films. In our study, we found that our newly designed benzimidazole-linked Cu (II)-phthalocyanine-based COFs (BICuPc-COFs) film can hardly formed with a regular aerobic oxidation method. Therefore, we developed a transfer dehydrogenation method with N-benzylideneaniline (BA) as a mild reagent. With this in hand, we successfully prepared a family of high crystalline BICuPc-COFs powders and films. Furthermore, both of these new BICuPc-COFs films showed high electrical conductivity (0.022-0.218 S/m), higher than most of the reported COFs materials. Due to the broad absorption and high conductivity of BICuPc-COFs, synaptic devices with small source-drain voltage (VDS=1 V) were fabricated with response light from visible to near-infrared. Based on these findings, we expect this study will provide a new perspective for the application of conducting heterocycle-linked COFs in synaptic devices.

Prediction of the Tribological Properties of Polytetrafluoroethylene Composites Based on Experiments and Machine Learning.

Because of the complex nonlinear relationship between working conditions, the prediction of tribological properties has become a difficult problem in the field of tribology. In this study, we employed three distinct machine learning (ML) models, namely random forest regression (RFR), gradient boosting regression (GBR), and extreme gradient boosting (XGBoost), to predict the tribological properties of polytetrafluoroethylene (PTFE) composites under high-speed and high-temperature conditions. Firstly, PTFE composites were successfully prepared, and tribological properties under different temperature, speed, and load conditions were studied in order to explore wear mechanisms. Then, the investigation focused on establishing correlations between the friction and wear of PTFE composites by testing these parameters through the prediction of the friction coefficient and wear rate. Importantly, the correlation results illustrated that the friction coefficient and wear rate gradually decreased with the increase in speed, which was also proven by the correlation coefficient. In addition, the GBR model could effectively predict the tribological properties of the PTFE composites. Furthermore, an analysis of relative importance revealed that both load and speed exerted a greater influence on the prediction of the friction coefficient and wear rate.

Flexible Cages Enable Robust Supramolecular Elastomers.

Advances in modern industrial technology continue to place stricter demands on engineering polymeric materials, but simultaneously possessing superior strength and toughness remains a daunting challenge. Herein, a pioneering flexible cage-reinforced supramolecular elastomer (CSE) is reported that exhibits superb robustness, tear resistance, anti-fatigue, and shape memory properties, achieved by innovatively introducing organic imide cages (OICs) into supramolecular networks. Intriguingly, extremely small amounts of OICs make the elastomer stronger, significantly improving mechanical strength (85.0 MPa; ≈10-fold increase) and toughness (418.4 MJ m-3; ≈7-fold increase). Significantly, the cooperative effect of gradient hydrogen bonds and OICs is experimentally and theoretically demonstrated as flexible nodes, enabling more robust supramolecular networks. In short, the proposed strengthening strategy of adding flexible cages effectively balances the inherent conflict between material strength and toughness, and the prepared CSEs are anticipated to be served in large-scale devices such as TBMs in the future.

Molecular Design of Multifunctional Integrated Polymer Semiconductors with Intrinsic Stretchability, High Mobility, and Intense Luminescence.

Multifunctional semiconductors integrating unique optical, electrical, mechanical, and chemical characteristics are critical to advanced and emerging manufacturing technologies. However, due to the trade-off challenges in design principles, fabrication difficulty, defects in existing materials, etc., realizing multiple functions through multistage manufacturing is quite taxing. Here, an effective molecular design strategy is established to prepare a class of multifunctional integrated polymer semiconductors. The pyridal[1,2,3]triazole-thiophene co-structured tetrapolymers with full-backbone coplanarity and considerable inter/intramolecular noncovalent interactions facilitate short-range order and excellent (re)organization capability of polymer chains, providing stress-dissipation sites in the film state. The regioregular multicomponent conjugated backbones contribute to dense packing, excellent crystallinity, high crack onset strain over 100%, efficient carrier transport with mobilities exceeding 1 cm2  V-1  s-1 , and controllable near-infrared luminescence. Furthermore, a homologous blending strategy is proposed to further enhance the color-tunable luminescent properties of polymers while effectively retaining mechanical and electrical properties. The blended system exhibits excellent field-effect mobility (µ) and quantum yield (Φ), reaching a record Φ · µ of 0.43 cm2  V-1  s-1 . Overall, the proposed strategy facilitates a rational design of regioregular semicrystalline intrinsically stretchable polymers with high mobility and color-tunable intense luminescence, providing unique possibilities for the development of multifunctional integrated semiconductors in organic optoelectronics.

Photoinduced Nonvolatile Memory Transistor Based on Lead-Free Perovskite Incorporating Fused π-Conjugated Organic Ligands.

Perovskites field-effect transistors (PeFETs) have been intensively investigated for their application in detector and synapse. However, synapse based on PeFETs is still very difficult to integrate excellent charge carrier transporting ability, photosensitivity, and nonvolatile memory effects into one device, which is very important for developing bionic electronic devices and edge computing. Here, two-dimensional (2D) perovskites are synthesized by incorporating fused π-conjugated pyrene-O-ethyl-ammonium (POE) ligands and a systematic study is conducted to obtain enhanced performance and reliable PeFETs. The optimized (POE)2 SnI4 transistors display the hole mobility over 0.3 cm2  V-1  s-1 , high repeatability, and operational stability. Meanwhile, the derived photo memory devices show remarkable photoresponse, with a switching ratio higher than 105 , high visible light responsivity (>4 × 104  A W-1 ), and stable storage-erase cycles, as well as competitive retention performance (104  s). The photoinduced memory behavior can be benefiting from the insulating nature of quantum-well in 2D perovskite under dark and its excellent light sensitivity. The excellent photo memory behaviors have been maintained after 40 days in a N2 atmosphere. Finally, a 2D perovskite-only transistors with a multi-level memory behavior (16 distinct states) is described by controlling incident light pulse. This work provides broader attention toward 2D perovskite and optoelectronic application.

Spatially nanoconfined N-type polymer semiconductors for stretchable ultrasensitive X-ray detection.

Polymer semiconductors are promising candidates for wearable and skin-like X-ray detectors due to their scalable manufacturing, adjustable molecular structures and intrinsic flexibility. Herein, we fabricated an intrinsically stretchable n-type polymer semiconductor through spatial nanoconfinement effect for ultrasensitive X-ray detectors. The design of high-orientation nanofiber structures and dense interpenetrating polymer networks enhanced the electron-transporting efficiency and stability of the polymer semiconductors. The resultant polymer semiconductors exhibited an ultrahigh sensitivity of 1.52 × 104 µC Gyair-1 cm-2, an ultralow detection limit of 37.7 nGyair s-1 (comparable to the record-low value of perovskite single crystals), and polymer film X-ray imaging was achieved at a low dose rate of 3.65 µGyair s-1 (about 1/12 dose rate of the commercial medical chest X-ray diagnosis). Meanwhile, the hybrid semiconductor films could sustain 100% biaxial stretching strain with minimal degeneracy in photoelectrical performances. These results provide insights into future high-performance, low-cost e-skin photoelectronic detectors and imaging.

Supramolecular Oleogel-Impregnated Macroporous Polyimide for High Capacity of Oil Storage and Recyclable Smart Lubrication.

Developing smart lubrication materials to achieve recyclable and durable lubrication and excellent wear resistance under various running conditions has great significance in fields ranging from aerospace to advanced engineering machinery but has proven challenging. Herein, a supramolecular oleogel with reversible gel-to-liquid transition was impregnated into macroporous polyimide (MPPI-gel) to obtain a smart lubrication material, which exhibited recyclable smart lubrication with an enhanced oil content and oil retention. The self-assembly of the gelator in polyalphaolefin10 (PAO10) formed three-dimensional networks that encapsulated the PAO10 during the service process, and the MPPI-gel could exhibit a high oil retention (approximately 99%). The gel-to-liquid transition allows the lubricant to be extruded and transferred to the surface of the macroporous matrix (MPPI) under thermal-mechano-stimuli and vice versa. The extruded lubricant can be sucked back into the MPPI pores through the capillary force and recovered to the oleogel when removing the external stimuli. Due to the high oil content, high oil retention, and recyclable lubricant releasing/reabsorbing, MPPI-gel exhibited recyclable smart lubrication (at least 1852 cycles; each cycle lasted for 1 h), a stable coefficient of friction (∼0.06) under alternating conditions (the frequency varied from 1 to 20 Hz, and the load varied from 10 to 46 N), and long-term conditions (at least 10 days). Therefore, MPPI-gel holds the promise of realizing smart lubrication according to the external stimuli with both high oil storage and recyclable lubricant releasing/reabsorbing with the porous matrix.

Estimation of soybean yield parameters under lodging conditions using RGB information from unmanned aerial vehicles.

The estimation of yield parameters based on early data is helpful for agricultural policymakers and food security. Developments in unmanned aerial vehicle (UAV) platforms and sensor technology help to estimate yields efficiency. Previous studies have been based on less cultivars (<10) and ideal experimental environments, it is not available in practical production. Therefore, the objective of this study was to estimate the yield parameters of soybean (Glycine max (L.) Merr.) under lodging conditions using RGB information. In this study, 17 time point data throughout the soybean growing season in Nanchang, Jiangxi Province, China, were collected, and the vegetation index, texture information, canopy cover, and crop height were obtained by UAV-image processing. After that, partial least squares regression (PLSR), logistic regression (Logistic), random forest regression (RFR), support vector machine regression (SVM), and deep learning neural network (DNN) were used to estimate the yield parameters. The results can be summarized as follows: (1) The most suitable time point to estimate the yield was flowering stage (48 days), which was when most of the soybean cultivars flowered. (2) The multiple data fusion improved the accuracy of estimating the yield parameters, and the texture information has a high potential to contribute to the estimation of yields, and (3) The DNN model showed the best accuracy of training (R2=0.66 rRMSE=32.62%) and validation (R2=0.50, rRMSE=43.71%) datasets. In conclusion, these results provide insights into both best estimate period selection and early yield estimation under lodging condition when using remote sensing.

Estimating leaf area index using unmanned aerial vehicle data: shallow vs. deep machine learning algorithms.

Measuring leaf area index (LAI) is essential for evaluating crop growth and estimating yield, thereby facilitating high-throughput phenotyping of maize (Zea mays). LAI estimation models use multi-source data from unmanned aerial vehicles (UAVs), but using multimodal data to estimate maize LAI, and the effect of tassels and soil background, remain understudied. Our research aims to (1) determine how multimodal data contribute to LAI and propose a framework for estimating LAI based on remote-sensing data, (2) evaluate the robustness and adaptability of an LAI estimation model that uses multimodal data fusion and deep neural networks (DNNs) in single- and whole growth stages, and (3) explore how soil background and maize tasseling affect LAI estimation. To construct multimodal datasets, our UAV collected red-green-blue, multispectral, and thermal infrared images. We then developed partial least square regression (PLSR), support vector regression, and random forest regression models to estimate LAI. We also developed a deep learning model with three hidden layers. This multimodal data structure accurately estimated maize LAI. The DNN model provided the best estimate (coefficient of determination [R2] = 0.89, relative root mean square error [rRMSE] = 12.92%) for a single growth period, and the PLSR model provided the best estimate (R2 = 0.70, rRMSE = 12.78%) for a whole growth period. Tassels reduced the accuracy of LAI estimation, but the soil background provided additional image feature information, improving accuracy. These results indicate that multimodal data fusion using low-cost UAVs and DNNs can accurately and reliably estimate LAI for crops, which is valuable for high-throughput phenotyping and high-spatial precision farmland management.

Cobweb-like Structural Stimuli-Responsive Composite with Oil Warehouse and Transportation System for Oil Storage and Recyclable Smart-Lubrication.

Despite recent advances in the stimuli-responsive composites for oil storage and smart lubrication, achieving the high oil storage and recyclable smart-lubrication remains a challenge. Herein, a novel cobweb-like structural system consisting of oil warehouse and transportation system was designed and prepared and it shows high capacity of oil storage and recyclable smart-lubrication. Hollow SiO2 microspheres grated of KH550 and porous polyimide (PPI) were used as oil warehouse and pipeline, respectively, to build the smart system. Because of the novel structure, the composites can keep both high oil-content and oil-retention. Applying stimuli on materials resulted in lubricants releasing on the contact surface which can reduce the friction and wear during sliding. However, removing stimuli, the capillary force induced the sucking back of lubricant into the interior of composites through interconnected small pores of PPI. On the basis of high oil storage and stimuli-responsive performance, the composites can be used for recyclable smart-lubrication. The composites showed remarkable lubricating properties (coefficient of friction 0.056 and Ws 3.55 × 10-7 mm3 N-1 m-1) when the content of KHSM (hollow silica microspheres grated of KH550 (3-Aminopropyltriethoxysilane)) was 1.5 wt % by subjecting it to macroscopic pin-on-disc friction tests. Therefore, cobweb-like structural composites with oil warehouse and transportation system hold the promise for formulating of high oil storage and recyclable smart-lubrication.