Experimental and Computational Evaluation of Self-Assembled Morphologies in Diblock Janus Bottlebrush Copolymers.

Diblock Janus-type "A-branch-B" bottlebrush copolymers (di-JBBCPs) consist of a backbone with alternating A and B side chains, in contrast to the side chain arrangement of conventional bottlebrush copolymers. As a result, A and B blocks of di-JBBCPs can microphase-separate perpendicular to the backbone, which is located at the interface between the two blocks. A reparametrized dissipative particle dynamics (DPD) model is used to theoretically investigate the self-assembly of di-JBBCPs and to compare with the experimental results of a range of polystyrene-branch-polydimethylsiloxane di-JBBCPs. The experimentally formed cylinder, gyroid, and lamellar morphologies showed good correspondence with the model phase diagram, and the effect of changing volume fraction and backbone length is revealed. The DPD model predicts a bulk-stable perforated lamella morphology together with two unconventional spherical phases, the Frank-Kasper A15 spheres and the hexagonally close-packed spheres, indicating the diversity of morphologies available from complex BCP molecular architectures.

Hierarchically engineered nanostructures from compositionally anisotropic molecular building blocks.

The inability to synthesize hierarchical structures with independently tailored nanoscale and mesoscale features limits the discovery of next-generation multifunctional materials. Here we present a predictable molecular self-assembly strategy to craft nanostructured materials with a variety of phase-in-phase hierarchical morphologies. The compositionally anisotropic building blocks employed in the assembly process are formed by multicomponent graft block copolymers containing sequence-defined side chains. The judicious design of various structural parameters in the graft block copolymers enables broadly tunable compositions, morphologies and lattice parameters across the nanoscale and mesoscale in the assembled structures. Our strategy introduces advanced design principles for the efficient creation of complex hierarchical structures and provides a facile synthetic platform to access nanomaterials with multiple precisely integrated functionalities.

The nonlinear effect of new urbanization on water pollutant emissions: Empirical analysis based on the panel threshold model.

Rapid urbanization has led to a significant increase in water consumption and wastewater discharge. Balancing the relationship between urbanization development and water pollutants emissions is crucial for the sustainable development of the country. Given the uneven regional economic development and resource distribution in China, exploring the relationship between new urbanization and water pollution emissions cannot be limited to a single perspective such as population urbanization. This study developed a comprehensive evaluation index system for new urbanization level. Based on data from 30 provincial-level regions in China from 2006 to 2020, a Panel Threshold Regression Model (PTRM) was used to explore the nonlinear relationship between the new urbanization level and water pollution discharge. The research results show that China's new urbanization level (NUBL) and its subsystems, including population urbanization (P-NUBL), economic urbanization (E-NUBL), and spatial urbanization (SP-NUBL), all have a double threshold effect on chemical oxygen demand (COD) emissions. The promoting effect of NUBL and E-NUBL on COD emissions gradually increased in the later stage of the study. P-NUBL and SP-NUBL show a trend of inhibiting COD emissions after crossing the dual threshold values. Social urbanization (S-NUBL) and ecological urbanization (EL-NUBL) had no threshold effect, but they also had a promoting effect on COD emissions. In addition, the speed of new urbanization in eastern China was significantly faster than that in central and western China, with provinces such as Beijing, Shanghai, and Jiangsu being the first to enter the high threshold stage. The central region began to gradually enter the middle threshold stage, but provinces such as Hebei, Henan, and Anhui are still in the high pollution and high emission stage. The level of new urbanization in western China is relatively low, and future development should prioritize economic construction. Provinces with high thresholds and low water pollution emissions still need to be developed. The results of this study have important implications for promoting the harmonious development of water-saving and sustainable urban development in China.

Hyperspectral Imaging as a Potential Online Detection Method of Microplastics.

Microplastic pollution in aquatic environment has raised concern and as a result a number of studies have recently been published to find solutions for its rapid increase. Different methods have been proposed for microplastic identification. Spectral imaging shows a lot of promise for polymer identification; however, the identification time needs to be improved. Hyperspectral imaging (HSI) combined with chemometric analysis can reduce the identification times. In this study, we provide a review of recent studies related to polymer identification using HSI with a focus on the adopted classification algorithm and its factors for the online implementation of HSI. Furthermore, we review the limit of detection by HSI and the effect of particle size on classification accuracy. Additionally, performance of this method for various types of samples is also discussed. We conclude that HSI is possible to be a fast alternative for online microplastic detection.

The Identification of Spherical Engineered Microplastics and Microalgae by Micro-hyperspectral Imaging.

Based on the micro-hyperspectral imaging technique, spherical engineered microplastic (polyethylene, 10-45 µm) and microalgae (Isochrysis galbana) (4-7 µm) were identified. In transmittance mode of MHSI, micro image cubes from 400 to 1000 nm were obtained from slides containing MP and MA in thin seawater. Classifiers like Support Vector Machine (SVM(Radial Basis Function (RBF))), Least Squares Support Vector Machine (LSSVM(RBF)), k-Nearest Neighbors, etc. were adopted and compared to classify MP and MA. In order to expand the imaging range of micro imaging, image stitching technology was adopted. In allusion to the stitched image cube, SVM(RBF) is suggested for the identification of MA and MP, with recall and precision > 0.86. The above results demonstrate that the MHSI is a promising technique, which can detect MPs with particle size Limit of Detection of 10-45 µm, and it is potential to further expand this LOD.

Parent B2 N2 -Perylenes with Different BN Orientations.

Introducing BN units into polycyclic aromatic hydrocarbons expands the chemical space of conjugated materials with novel properties. However, it is challenging to achieve accurate synthesis of BN-PAHs with specific BN positions and orientations. Here, three new parent B2 N2 -perylenes with different BN orientations are synthesized with BN-naphthalene as the building block, providing systematic insight into the effects of BN incorporation with different orientations on the structure, (anti)aromaticity, crystal packing and photophysical properties. The intermolecular dipole-dipole interaction shortens the π-π stacking distance. The crystal structure, (anti)aromaticity, and photophysical properties vary with the change of BN orientation. The revealed BN doping effects may provide a guideline for the synthesis of BN-PAHs with specific stacking structures, and the synthetic strategy employed here can be extended toward the synthesis of larger BN-embedded PAHs with adjustable BN patterns.

Engineering Supramolecular Polymer Conformation for Efficient Carbon Nanotube Sorting.

Supramolecular polymer sorting is a promising approach to separating single-walled carbon nanotubes (CNTs) by electronic type. Unlike conjugated polymers, they can be easily removed from the CNTs after sorting by breaking the supramolecular bonds, allowing for isolation of electronically pristine CNTs as well as facile recycling of the sorting polymer. However, little is understood about how supramolecular polymer properties affect CNT sorting. Herein, chain stoppers are used to engineer the conformation of a supramolecular sorting polymer, thereby elucidating the relationship between sorting efficacy and polymer conformation. Through NMR and UV-vis spectroscopy, small-angle X-ray scattering (SAXS), and thermodynamic modeling, it is shown that this supramolecular polymer exhibits ring-chain equilibrium, and that this equilibrium can be skewed toward chains by the addition of chain stoppers. Furthermore, by controlling the stopper-monomer ratio, the sorting yield can be doubled from 7% to 14% without compromising the semiconducting purity (>99%) or properties of sorted CNTs.

Rapid Construction of Fold-Line-Shaped BN-Embedded Polycyclic Aromatic Compounds through Diels-Alder Reaction.

The Diels-Alder reaction strategy that can rapidly extend the conjugated backbone was applied to facilely synthesize fold-line, coplanar BN-embedded polycyclic aromatic hydrocarbons from simple small BN compounds. The molecular structures and packing modes of these BN-embedded acenes were confirmed by single-crystal X-ray diffraction. Their electronic and photophysical properties were studied by using UV-vis, fluorescence spectroscopy, electrochemical cyclic voltammetry, and density functional theory calculations. These results demonstrate the efficiency and feasibility of this synthetic strategy.

BN-Embedded Tetrabenzopentacene: A Pentacene Derivative with Improved Stability.

Considerable efforts have been devoted to achieving stable acene derivatives for electronic applications; however, the instability is still a major issue for such derivatives. To achieve higher stability with minimum structural change, CC units in the acenes were replaced with isoelectronic BN units to produce a novel BN-embedded tetrabenzopentacene (BNTBP). BNTBP, with a planar structure, is highly stable to air, moisture, light, and heat. Compared with its carbon analogue tetrabenzopentacene (TBP), BN embedment lowered the highest occupied molecular orbital (HOMO) energy level of BNTBP, changed the orbital distribution, and decreased the HOMO orbital coefficients at the central carbon atoms, which stabilize BNTBP molecules upon exposure to oxygen and sunlight. The single-crystal microribbons of BNTBP exhibited good performance in field-effect transistors (FETs). The high stability and good mobility of BNTBP indicates that BN incorporation is an effective approach to afford stable large-sized acenes with desired properties.

Interactive Allocation of Water Pollutant Initial Emission Rights in a Basin under Total Amount Control: A Leader-Follower Hierarchical Decision Model.

The initial emission rights allocation is the key measure to achieve the goal of total amount control and deepen the emission trading system. Although many studies have focused on the modeling of initial emission rights allocation, such as using game theory and multi-objective optimization methods, few studies have observed the hierarchical relationship of mutual interference and restriction between watershed management agency and local governments in each subarea during allocation. This relationship directly affects the rationality of the results of regional emission rights allocation. In this study, a leader-follower hierarchical decision model (LFHDM) for allocating initial emission rights in a basin is developed. Based on the bilevel programming approach, the model simulates the interactive decision-making process between the watershed management agency of the upper-level model (LFHDM-U) and the local government of the lower-level model (LFHDM-L) in the allocation under total amount control. A case study of China's Yellow River Basin is conducted to demonstrate the feasibility and practicality of the model. Findings reveal that, compared with the single-level model, the developed LFHDM has higher satisfaction with the allocation scheme. Under different scenarios, the overall satisfaction of the configuration schemes of COD and NH3-N in each province and autonomous region remains above 0.9. In addition, the allocation volumes of COD and NH3-N in each province of the Yellow River Basin in planning year increase with the enhancement of allowable assimilative capacity of water bodies, but the interval gap of satisfaction with allocation schemes gradually narrows. It shows that when the allowable assimilation capacity of a water body is low, the decision-making of the allocation scheme needs to be more cautious. Moreover, for the Yellow River Basin, apart from Qinghai and Sichuan, the task of reducing water pollutants in other provinces in the next few years is very arduous. The average reduction of total COD and NH3-N in the basin is about 48% and 46%, respectively.

Reversible Morphology Locking via Metal Infiltration in a Block Copolymer.

Metal infiltration from an acid solution of a metal precursor into the poly(2-vinylpyridine) (P2VP) microdomains of a polystyrene-b-P2VP block copolymer is shown to reduce the uptake of solvent vapor during a subsequent solvent annealing process, locking the morphology of the self-assembled microdomains. The amount of metal, here Pt, incorporated into the P2VP increases with both metal precursor [PtCl4]2- and hydrochloric acid concentrations, reaching 0.83 Pt atom per pyridine unit. The metal is then exfiltrated using a KOH + ethylenediaminetetraacetic acid disodium salt dihydrate (Na2EDTA) complexing solution, which restores solvent uptake and unlocks the morphology. The reversibility of the metal infiltration and morphology locking is demonstrated in a multistage annealing process and is confirmed for Fe as well as Pt. Reversible locking and unlocking of block copolymer microdomain morphologies expand their utility for nanofabrication processes by allowing the morphology to be fixed during subsequent process steps.

Pendant Group Modifications Provide Graft Copolymer Silicones with Exceptionally Broad Thermomechanical Properties.

Graft copolymers offer a versatile platform for the design of self-assembling materials; however, simple strategies for precisely and independently controlling the thermomechanical and morphological properties of graft copolymers remain elusive. Here, using a library of 92 polynorbornene-graft-polydimethylsiloxane (PDMS) copolymers, we discover a versatile backbone-pendant sequence-control strategy that addresses this challenge. Small structural variations of pendant groups, e.g., cyclohexyl versus n-hexyl, of small-molecule comonomers have dramatic impacts on order-to-disorder transitions, glass transitions, mechanical properties, and morphologies of statistical and block silicone-based graft copolymers, providing an exceptionally broad palette of designable materials properties. For example, statistical graft copolymers with high PDMS volume fractions yielded unbridged body-centered cubic morphologies that behaved as soft plastic crystals. By contrast, lamellae-forming graft copolymers provided robust, yet reprocessable silicone thermoplastics (TPs) with transition temperatures spanning over 160 °C and elastic moduli as high as 150 MPa despite being both unentangled and un-cross-linked. Altogether, this study reveals a new pendant-group-mediated self-assembly strategy that simplifies graft copolymer synthesis and enables access to a diverse family of silicone-based materials, setting the stage for the broader development of self-assembling materials with tailored performance specifications.

Proceeding the categorization of microplastics through deep learning-based image segmentation.

Microplastics (MPs) have been recognized as prominent anthropogenic pollutants that inflict significant harm to marine ecosystems. Various approaches have been proposed to mitigate the risks posed by MPs. Gaining an understanding of the morphology of plastic particles can provide valuable insights into the source and their interaction with marine organisms, which can assist the development of response measures. In this study, we present an automated technique for identifying MPs through segmentation of MPs in microscopic images using a deep convolutional neural network (DCNN) based on a shape classification nomenclature framework. We used MP images from diverse samples to train a Mask Region Convolutional Neural Network (Mask R-CNN) based model for classification. Erosion and dilation operations were added to the model to improve segmentation results. On the testing dataset, the mean F1-score (F1) of segmentation and shape classification was 0.7601 and 0.617, respectively. These results demonstrate the potential of proposed method for the automatic segmentation and shape classification of MPs. Furthermore, by adopting a specific nomenclature, our approach represents a practical step towards the global standardization of MPs categorization criteria. This work also identifies future research directions to improve accuracy and further explore the possibilities of using DCNN for MPs identification.

Emergence of layered nanoscale mesh networks through intrinsic molecular confinement self-assembly.

Block copolymer self-assembly is a powerful tool for two-dimensional nanofabrication; however, the extension of this self-assembly concept to complex three-dimensional network structures is limited. Here we report a simple method to experimentally generate three-dimensional layered mesh morphologies through intrinsic molecular confinement self-assembly. We designed triblock bottlebrush polymers with two Janus domains: one perpendicular and one parallel to the polymer backbone. The former enforces a lamellar superstructure that intrinsically confines the intralayer self-assembly of the latter, giving rise to a mesh-like monoclinic (54°) M15 network substructure with excellent long-range order, as well as a tetragonal (90°) T131 mesh. Numerical simulations show that the spatial constraints exerted on the polymer backbone drive the assembly of M15 and yield T131 in the strong segregation regime. This work demonstrates that intrinsic molecular confinement is a viable path to bottom-up assembly of new geometrical phases of soft matter, extending the capabilities of block copolymer nanofabrication.

Upconversion boosting pollutants degradation efficiency in wide-spectrum responsive photocatalysts.

Recently, the composite photocatalysts coupled with upconversion materials have received widespread attention due to higher utilization efficiency of solar energy in a wide-spectrum range. Novel heterojunction photocatalysts of CoWO4@NaYF4:Yb3+,Er3+ were designed and developed herein. The structural characterization, morphology and elemental composition analysis demonstrated that heterojunctions between CoWO4 and NaYF4:Yb3+,Er3+ were indeed formed in the composite photocatalysts. Moreover, CoWO4@NaYF4:Yb3+,Er3+ heterojunction photocatalysts exhibited higher pollutants degradation efficiency. Especially, a great enhancement of +87% on the photocatalytic activity was achieved in the heterojunction photocatalyst of 60CoWO4-NaYF4:Yb3+,Er3+ compared with pure CoWO4. The dominant radicals generated from the heterojunction photocatalysts were confirmed as the photo-generated holes (h+) and hydroxyl radicals (⋅OH) through the radical species trapping experiments and fluorescence detection, which is fully in line with the expected band structure characteristics of CoWO4. Eventually, an underlying mechanism was proposed that the enhanced photocatalytic activity should be attributed to the wide-spectrum responsive features of CoWO4@NaYF4:Yb3+,Er3+ heterojunction photocatalysts. Within the heterostructures, CoWO4 photocatalyst can absorb both the UV-Vis light due to its narrow bandgap and the Near-Infrared energy through the upconversion NaYF4:Yb3+,Er3+, thereby utilizing solar energy more efficiently in a wide-spectrum range for photocatalytic reactions.

Metallic Nanomeshes Fabricated by Multimechanism Directed Self-Assembly.

The directed self-assembly of block copolymers (BCPs) is a powerful motif for the continued scaling of feature sizes for nanoscale devices. A multimechanism directed self-assembly (MMDSA) method is described that generates orthogonal meshes from a polystyrene-b-poly-2-vinylpyridine BCP that is subsequently metallized with Pt. The MMDSA process takes advantage of three different mechanisms, trench wall guidance, edge nucleation, and underlayer guidance, to align the mesh with respect to substrate features. The mechanisms and their interactions are investigated via both experiments and dissipative particle dynamics simulations. MMDSA is applied to produce well-aligned conductive nanomeshes and then is extended to fabricate multicomponent metallic structures with 2D/3D hybrid morphologies.

Integrating Band Engineering and the Flexoelectric Effect Induced by a Composition Gradient for High Photocurrent Density in Bismuth Ferrite Films.

Photovoltaic energy as one of the important alternatives to traditional fossil fuels has always been a research hot spot in the field of renewable and clean solar energy. Very recently, the anomalous ferroelectric photovoltaic effect in multiferroic bismuth ferrite (BiFeO3) has attracted much attention due to the above-bandgap photovoltage and switchable photocurrent. However, its photocurrent density mostly in the magnitudes of µA/cm2 resulted in a poor power conversion efficiency, which severely hampered its practical application as a photovoltaic device. In this case, a novel approach was designed to improve the photocurrent density of BiFeO3 through the cooperative effect of the gradient distribution of oxygen vacancies and consequently induced the flexoelectric effect realized in the (La, Co) gradient-doped BiFeO3 multilayers. Subsequent results and analysis indicated that the photocurrent density of the gradient-doped multilayer BiFeO3 sample was nearly 3 times as much as that of the conventional doped single-layer sample. Furthermore, a possible mechanism was proposed herein to demonstrate roles of band engineering and the flexoelectric effect on the photovoltaic performance of the gradient-doped BiFeO3 film.

Machine Learning Predictions of Block Copolymer Self-Assembly.

Directed self-assembly of block copolymers is a key enabler for nanofabrication of devices with sub-10 nm feature sizes, allowing patterning far below the resolution limit of conventional photolithography. Among all the process steps involved in block copolymer self-assembly, solvent annealing plays a dominant role in determining the film morphology and pattern quality, yet the interplay of the multiple parameters during solvent annealing, including the initial thickness, swelling, time, and solvent ratio, makes it difficult to predict and control the resultant self-assembled pattern. Here, machine learning tools are applied to analyze the solvent annealing process and predict the effect of process parameters on morphology and defectivity. Two neural networks are constructed and trained, yielding accurate prediction of the final morphology in agreement with experimental data. A ridge regression model is constructed to identify the critical parameters that determine the quality of line/space patterns. These results illustrate the potential of machine learning to inform nanomanufacturing processes.

Single spectral imagery and faster R-CNN to identify hazardous and noxious substances spills.

The automatic identification (location, segmentation, and classification) by UAV- based optical imaging of spills of transparent floating Hazardous and Noxious Substances (HNS) benefits the on-site response to spill incidents, but it is also challenging. With a focus on the on-site optical imaging of HNS, this study explores the potential of single spectral imaging for HNS identification using the Faster R-CNN architecture. Images at 365 nm (narrow UV band), blue channel images (visible broadband of ∼400-600 nm), and RGB images of typical HNS (benzene, xylene, and palm oil) in different scenarios were studied with and without Faster R-CNN. Faster R-CNN was applied to locate and classify the HNS spills. The segmentation using Faster R-CNN-based methods and the original masking methods, including Otsu, Max entropy, and the local fuzzy thresholding method (LFTM), were investigated to explore the optimal wavelength and corresponding image processing method for the optical imaging of HNS. We also compared the classification and segmentation results of this study with our previously published studies on multispectral and whole spectral images. The results demonstrated that single spectral UV imaging at 365 nm combined with Faster R-CNN has great potential for the automatic identification of transparent HNS floating on the surface of the water. RGB images and images using Faster R-CNN in the blue channel are capable of HNS segmentation.