Structural Features Research Articles

G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction

Developing new drugs is costly, time-consuming, and risky. Drug-target affinity (DTA), indicating the binding capability between drugs and target proteins, is a crucial indicator for drug development. Accurately predicting interaction strength between new drug-target pairs by analyzing previous experiments aids in screening potential drug molecules, repurposing them, and developing safe and effective medicines. Existing computational models for DTA prediction rely on strings or single-graph neural networks, lacking consideration of protein structure and molecular semantic information, leading to limited accuracy. Our experiments demonstrate that string-based methods may overlook protein conformations, causing a high root mean square error (RMSE) of 3.584 in affinity due to a lack of spatial context. Single graph networks also underperform on topology features, with a 6% lower confidence interval (CI) for activity classification. Absent semantic information also limits generalization across diverse compounds, resulting in 18% increment in RMSE and 5% in misclassifications within quantifications study, restricting potential drug discovery. To address these limitations, we propose G-K BertDTA, a novel framework for accurate DTA prediction incorporating protein features, molecular semantic features, and molecular structural information. In this proposed model, we represent drugs as graphs, with a GIN employed to learn the molecular topological information. For the extraction of protein structural features, we utilize a DenseNet architecture. A knowledge-based BERT semantic model is incorporated to obtain rich pre-trained semantic embeddings, thereby enhancing the feature information. We extensively evaluated our proposed approach on the publicly available benchmark datasets (i.e., KIBA and Davis), and experimental results demonstrate the promising performance of our method, which consistently outperforms previous state-of-the-art approaches. Code is available at https://github.com/AmbitYuki/G-K-BertDTA.

Application of co-crystallization method for the production of ammonium perchlorate/ammonium nitrate oxidizer for solid rocket propellants

Efforts in the quest for greener alternatives to ammonium perchlorate (AP) as a solid rocket propellant oxidizer have intensified due to the associated health and environmental concerns. AP exceptional performance in propulsion is accompagnied by its environmental concerns, necessitating the exploration of greener alternatives. Ammonium nitrate (AN) has emerged as a promising substitute, offering cleaner combustion products and cost-effectiveness. AN-based propellants face challenges such as hygroscopicity and structural instability. This study aims to reconcile the latter challenges by employing a co-crystallization process that embodies a synergistic association of AP and AN, culminating in a novel molecule that harmonizes their distinct advantages and mitigates their individual shortcomings. Optimizing the solvent/antisolvent co-crystallization process through a variation of the antisolvent temperature Ta and antisolvent-to-solvent ratio Ra/s allows to assess their impact on the co-crystallization process of AP and AN and to attain the desired and optimal co-crystal formation conditions. Various characterization techniques were applied to gain deep insights into the co-crystal formation mechanism, its thermal behavior, morphological and structural features together with its thermal decomposition kinetics. Maintaining a low Ta and a high Ra/s (Ta = 15 °C, Ra/s = 11) yielded the design of AP/AN that demonstrates typical indications of the cocrystal formation. The AP/AN cocrystal formation elevated the decomposition temperature by about 18 °C compared to the physical mixture. Furthermore, the combination of AP and AN demonstrates a non-negligible decrease in the HTD temperature compared to that of pure AP by about 110 °C. The kinetic study demonstrated a 50 kJ/mol increase in the activation energy of the AP/AN cocrystal compared to that of the physical mixture. The present research addresses the critical gap in the literature by investigating the co-crystallization of AP and AN that not only improves the safety and environmental impact of solid propellants but also enhances their overall performance and stability.

Oxalate-mediated synthesis of hybrid nickel cobalt-based nanostructures for boosting water and urea electrooxidation efficiency

The utilization of urea during electrolysis presents an appealing option for anodic oxidation, offering environmental benefits by converting waste into energy. The present work focuses on synthesizing mixed transition metal oxide (MMO) nanostructures composed of nickel rich nickel cobaltite electrode materials. Interesting, cobalt rich nickel cobaltite have been widely studied; however, the nickel-rich nickel cobaltite remains relatively unexplored. For stabilization of nickel rich nickel cobaltite, we have designed a process for synthesizing three-dimensional (3D) Ni-Co oxalate as a precursor via the coprecipitation method using diammonium oxalate monohydrate as a precipitating agent. The as obtained precursor was further subjected to thermal decomposition which results in the formation of Ni2CoO4. Microstructural analysis of Ni-Co oxalate indicates that oxalate-assisted synthesis leads to anisotropic ultrafine nanoparticle growth, forming hierarchically arranged 3D microflowers. Interestingly, the use of NaOH as a precipitating agent induces a significant change in the morphology of Ni2CoO4, (nanoparticles). Furthermore, the effect of morphology changes over the electrochemical performance was investigated. Ni2CoO4 microflower demonstrates an overpotential of 330 mV@25 mA/cm2 for O2 and −190 mV@10 mA/cm2 for H2 production during water oxidation in 1 M KOH, exhibiting excellent stability for up to 24 h. During urea oxidation, the potential required by Ni2CoO4 microflowers was 1.31 V@10 mA/cm2. Ni2CoO4 nanoparticles' performance is significantly poor during water and urea oxidation compared to the microflower counterpart. Therefore, this work provides detailed insights into designing MMOs with high porosity, increased exposed active sites, and unique structural features that enhance electrocatalytic activity.

Polysubstitution Induced Centrosymmetric-to-Noncentrosymmetric Structural Transformation and Nonlinear-Optical Behavior: The Case of Na0.45Ag0.55Ga3Se5.

Obtaining compounds with large nonlinear-optical (NLO) coefficients and wide band gaps is challenging due to their competitive requirements for chemical bonds. Herein, the first member with mixed cations on the A site in the A-M3-Q5 or A-Ag-M6-Q10 (A = alkali metal; M = Ga, In; Q = S, Se, Te) family, viz. Na0.45Ag0.55Ga3Se5 (NAGSe), was obtained by a solid-state reaction. Its structure features [GaSe4] tetrahedra built three-dimensional {[Ga3Se5]-}∞ network, with Na and Na/Ag cations located at the octahedral cavities. Noncentrosymmetric (R32) NAGSe can also be transformed from centrosymmetric RbGa3S5 (P21/c) via multiple-site cosubstitution. NAGSe exhibits the highest NLO response (1.9 × AGS) in the A-Ag-M-Q family. Crystal structure analysis and theoretical calculations suggest that the NLO response is mainly contributed by the regularly arranged [GaSe4] units. This work enriches the exploration of the undeveloped A-M3-Q5 or A-Ag-M6-Q10 family as potential infrared NLO materials.

Structure and agency in resistance to schooling: Class, race, and the reproduction of unequal outcomes

For low-income and marginalized racialized minority youth, declining prospects of mobility can undermine favorable attitudes toward schooling if adolescents anticipate limited utility in schooling. We find that adolescents' awareness of race and class inequality affects a complex set of attitudes toward schooling, and that these attitudes contribute to outcomes varying by race/ethnicity and class. We capitalize on a unique longitudinal dataset with a random stratified sample of 1428 Black and White high school graduates from a large school system. Using surveys and administrative data, we show how structural factors and student characteristics shape educational attitudes; and then how these attitudes, school structural features, student, family, and neighborhood factors predict educational outcomes. We find the common ground between Willis' resistance theory emphasizing class and Ogbu’s cultural-ecological model focusing on race. Results provide greater conceptual clarity for core constructs associated with both theories of resistance.

Tailoring thin film composite membranes for clean water production: A study on structural variations and predictive insights using machine learning

This study focuses on the fabrication of three thin film composite membranes by interfacial polymerization on polyacrylonitrile support. The objective is to explore the influence of different monomers on structural and functional features of resultant membranes for treating saline water and removing micropollutants. A new linear aliphatic amine 4A–2E was used as an aqueous monomer for the first time. Trimesoyl chloride, isophthaloyl chloride, and terephthaloyl chloride were used as organic phase crosslinkers. The resulting membranes exhibited distinctive structural features such as varied roughness and hydrophilicity, which correlated with their functional performance. The membrane roughness for 4A–2E-TPC@PAN/PET reached the highest value of Ra = 56 nm among the fabricated membranes. Moreover, permeate flux of 32, 56, and 49 L m−2 h−1 at 25 bar was measured for 4A–2E-TPC@PAN/PET, 4A–2E-IPC@PAN/PET, and 4A–2E-TMC@PAN/PET, respectively. Notably, 4A–2E-TPC@PAN/PET membrane exhibited 98% rejection of MgCl2 among the tested divalent salts. Furthermore, the membrane displayed high rejections of micropollutants, with 85% rejection for Amitriptyline. Upon chlorine exposure, the 4A–2E-TPC@PAN/PET membrane proved more resistant than both 4A–2E-IPC@PAN/PET and 4A–2E-TMC@PAN/PET membranes. Machine learning predictive insight was developed using Matérn Gaussian Process Regression model to simulate flux before and after chlorination of membranes. The results in both training and testing proved reliable predictive insight with 99% accuracy in terms of Nash Sutcliffe Efficiency. Hence, current work highlights the use of a linear amine 4A–2E and TPC crosslinker to fabricate a dense polyamide membrane and the application of ML algorithms to desalination data makes it possible to predict the membrane performance.

DRFNet: a deep radiomic fusion network for nAMD/PCV differentiation in OCT images.

Objective.Neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) present many similar clinical features. However, there are significant differences in the progression of nAMD and PCV. and it is crucial to make accurate diagnosis for treatment. In this paper, we propose a structure-radiomic fusion network (DRFNet) to differentiate PCV and nAMD in optical coherence tomography (OCT) images.Approach.The subnetwork (RIMNet) is designed to automatically segment the lesion of nAMD and PCV. Another subnetwork (StrEncoder) is designed to extract deep structural features of the segmented lesion. The subnetwork (RadEncoder) is designed to extract radiomic features from the segmented lesions based on radiomics. 305 eyes (155 with nAMD and 150 with PCV) are included and manually annotated CNV region in this study. The proposed method was trained and evaluated by 4-fold cross validation using the collected data and was compared with the advanced differentiation methods.Main results.The proposed method achieved high classification performace of nAMD/PCV differentiation in OCT images, which was an improvement of 4.68 compared with other best method.Significance. The presented structure-radiomic fusion network (DRFNet) has great performance of diagnosing nAMD and PCV and high clinical value by using OCT instead of indocyanine green angiography.

Bacterial hemophilin homologs and their specific type eleven secretor proteins have conserved roles in heme capture and are diversifying as a family.

Cellular life relies on enzymes that require metals, which must be acquired from extracellular sources. Bacteria utilize surface and secreted proteins to acquire such valuable nutrients from their environment. These include the cargo proteins of the type eleven secretion system (T11SS), which have been connected to host specificity, metal homeostasis, and nutritional immunity evasion. This Sec-dependent, Gram-negative secretion system is encoded by organisms throughout the phylum Proteobacteria, including human pathogens Neisseria meningitidis, Proteus mirabilis, Acinetobacter baumannii, and Haemophilus influenzae. Experimentally verified T11SS-dependent cargo include transferrin-binding protein B (TbpB), the hemophilin homologs heme receptor protein C (HrpC), hemophilin A (HphA), the immune evasion protein factor-H binding protein (fHbp), and the host symbiosis factor nematode intestinal localization protein C (NilC). Here, we examined the specificity of T11SS systems for their cognate cargo proteins using taxonomically distributed homolog pairs of T11SS and hemophilin cargo and explored the ligand binding ability of those hemophilin cargo homologs. In vivo expression in Escherichia coli of hemophilin homologs revealed that each is secreted in a specific manner by its cognate T11SS protein. Sequence analysis and structural modeling suggest that all hemophilin homologs share an N-terminal ligand-binding domain with the same topology as the ligand-binding domains of the Haemophilus haemolyticus heme binding protein (Hpl) and HphA. We term this signature feature of this group of proteins the hemophilin ligand-binding domain. Network analysis of hemophilin homologs revealed five subclusters and representatives from four of these showed variable heme-binding activities, which, combined with sequence-structure variation, suggests that hemophilins are diversifying in function.IMPORTANCEThe secreted protein hemophilin and its homologs contribute to the survival of several bacterial symbionts within their respective host environments. Here, we compared taxonomically diverse hemophilin homologs and their paired Type 11 secretion systems (T11SS) to determine if heme binding and T11SS secretion are conserved characteristics of this family. We establish the existence of divergent hemophilin sub-families and describe structural features that contribute to distinct ligand-binding behaviors. Furthermore, we demonstrate that T11SS are specific for their cognate hemophilin family cargo proteins. Our work establishes that hemophilin homolog-T11SS pairs are diverging from each other, potentially evolving into novel ligand acquisition systems that provide competitive benefits in host niches.

A cellulosomal double-dockerin module from Clostridium thermocellum shows distinct structural and cohesin-binding features.

Cellulosomes are intricate cellulose-degrading multi-enzymatic complexes produced by anaerobic bacteria, which are valuable for bioenergy development and biotechnology. Cellulosome assembly relies on the selective interaction between cohesin modules in structural scaffolding proteins (scaffoldins) and dockerin modules in enzymes. Although the number of tandem cohesins in the scaffoldins is believed to determine the complexity of the cellulosomes, tandem dockerins also exist, albeit very rare, in some cellulosomal components whose assembly and functional roles are currently unclear. In this study, we characterized the structure and mode of assembly of a tandem bimodular double-dockerin, which is connected to a putative S8 protease in the cellulosome-producing bacterium, Clostridium thermocellum. Crystal and NMR structures of the double-dockerin revealed two typical type I dockerin folds with significant interactions between them. Interaction analysis by isothermal titration calorimetry and NMR titration experiments revealed that the double-dockerin displays a preference for binding to the cell-wall anchoring scaffoldin ScaD through the first dockerin with a canonical dual-binding mode, while the second dockerin module was unable to bind to any of the tested cohesins. Surprisingly, the double-dockerin showed a much higher affinity to a cohesin from the CipC scaffoldin of Clostridium cellulolyticum than to the resident cohesins from C. thermocellum. These results contribute valuable insights into the structure and assembly of the double-dockerin module, and provide the basis for further functional studies on multiple-dockerin modules and cellulosomal proteases, thus highlighting the complexity and diversity of cellulosomal components.

In Situ Flash Synthesis of Ultra-High-Performance Metal Oxide Anode through Shunting Current-Based Electrothermal Shock.

The synthesis of anode materials plays an important role in determining the production efficiency, cost, and performance of lithium-ion batteries (LIBs). However, a low-cost, high-speed, scalable manufacturing process of the anode with the desired structural feature for practical technology adoption remains elusive. In this study, we propose a novel method called in situ flash shunt-electrothermal shock (SETS) which is controllable, fast, and energy-saving for synthesizing metal oxide-based materials. By using the example of direct electrothermal decomposition of ZIF-67 precursor loaded onto copper foil support, we achieve rapid (0.1-0.3 s) pyrolysis and generate porous hollow cubic structure material consisting of carbon-coated ultrasmall (10-15 nm) subcrystalline CoO/Co nanoparticles with controllable morphology. It was shown that CoO/Co@N-C exhibits prominent electrochemical performance with a high reversible capacity up to 1503.7 mA h g-1 after 150 cycles at 0.2 A g-1and stable capacities up to 434.1 mA h g-1 after 400 cycles at a high current density of 6 A g-1. This fabrication technique integrates the synthesis of active materials and the formation of electrode sheets into one process, thus simplifying the preparation of electrodes. Due to the simplicity and scalability of this process, it can be envisaged to apply it to the synthesis of metal oxide-based materials and to achieve large-scale production in a nanomanufacturing process.

Protein Homology Modeling in the Low Sequence Similarity Regime

Predicting the 3-D structure of a protein from its sequence based on a template protein structure is still one of the most exact modeling techniques present today. However, template-based modeling is heavily dependent on the selection of a single template structure and the sequence alignment between target and template. Mainly when the target and template sequence identity is low, the error from the alignment introduces larger errors to the model structure. An iterative method to correct such alignment mistakes is used in this study with a benchmark set from CASP in the extremely low sequence-identity regime. This is a protocol developed and tested before and it evaluates the alignment quality by building rough 3-D models for each alignment. Then by using a genetic algorithm it iteratively creates a new set of alignments. Since the method evaluates models, not sequence alignments, structural features are automatically incorporated into the alignment protocol. In the current study, models from structural alignment have been built by Modeller program to show the maximum possible quality of the model that can be obtained from that template structure with the iterative modeling protocol. Then the results and correctly aligned segments from the iterative modeling protocol are analyzed. Finally, it has been shown that if a good local fragment assessment scoring function is developed, the correctly aligned segments exist in the pool of alignments created by the protocol. Thus, the improvement of modeling in the low sequence identity regime is conceivable.

A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data

AbstractDisclosing the full potential of functional nanomaterials requires the optimization of synthetic protocols and an effective size screening tool, aiming at triggering their size-dependent properties. Here we demonstrate the successful combination of a wide-angle X-ray total scattering approach with a deep learning classifier for quantum dots sizing in both colloidal and dry states. This work offers a compelling alternative to the lengthy process of deriving sizing curves from transmission electron microscopy coupled with spectroscopic measurements, especially in the ultra-small size regime, where empirical functions exhibit larger discrepancies. The core of our algorithm is an all-convolutional neural network trained on Debye scattering equation simulations, incorporating atomistic models to capture structural and morphological features, and augmented with physics-informed perturbations to account for different predictable experimental conditions. The model performances are evaluated using both wide-angle X-ray total scattering simulations and experimental datasets collected on lead sulfide quantum dots, resulting in size classification accuracies surpassing 97%. With the developed deep learning size classifier, we overcome the need for calibration curves for quantum dots sizing and thanks to the unified modeling approach at the basis of the total scattering method implemented, we include simultaneously structural and microstructural aspects in the classification process. This algorithm can be complemented by incorporating input information from other experimental observations (e.g., small angle X-ray scattering data) and, after proper training with the pertinent simulations, can be extended to other classes of quantum dots, providing the nanoscience community with a powerful and broad tool to accelerate the development of functional (nano)materials.

Surface modification of ZrC dispersion-strengthened W under low energy He plasma irradiation

ZrC dispersion-strengthened W exhibits high strength/ductility, low ductile-to-brittle transition temperature, and excellent thermal shock resistance, making it a promising candidate plasma-facing material for future fusion devices. In this study, surface modification of 0.5 wt.% ZrC dispersion-strengthened W (WZrC) under low energy and high fluence He plasma irradiation at high temperature was presented. Under the energy of 90 eV and fluence ranging from 6 × 1024 He·m−2–2 × 1026 He·m−2 He irradiation at 920 °C, a typical fuzz nanostructure appeared on the W matrix of WZrC. The thickness of fuzz layer is proportional to the square root of He irradiation fluence. The fuzz showed comparable thickness and structural features to pure W, indicating limited effects of the particle’s addition on resistance to high fluence He irradiation at high temperatures. Under continuous He injection, the fuzz would grow extending onto the particle area, making the particle obscured. Besides, the erosion behavior of particles under He plasma irradiation has been investigated, which is thought to be dominated by a sputtering process. Under the He influence of 6 × 1024 He·m−2, only nanopores were observed in the surface region. With fluence increasing to 5 × 1025 He·m−2, the surface became relatively uneven with larger holes. W aggregated in spots and distributed on the surface of the particle, which might be the result of subthreshold sputtering and deposition. When fluence further increased to 2 × 1026 He·m−2, the particles were eroded completely and covered by the extended fuzz, forming cavities. In addition, distinctive layered nanotendrils were observed above the cavities, which were characterized to consist of inner W-riched skeletons and outer Zr-riched layers. It indicates that the layered nanotendrils should be the result of fuzz extension combined with particle sputtering/deposition.

Mixed‐Linker Strategy for the Construction of Sulfone‐Containing D–A–A Covalent Organic Frameworks for Efficient Photocatalytic Hydrogen Peroxide Production

AbstractThe solar‐driven photocatalytic production of hydrogen peroxide (H2O2) from water and oxygen using semiconductor catalysts offers a promising approach for converting solar energy into storable chemical energy. However, the efficiency of photocatalytic H2O2 production is often restricted by the low photo‐generated charge separation, slow surface reactions and inadequate stability. Here, we developed a mixed‐linker strategy to build a donor‐acceptor‐acceptor (D–A–A) type covalent organic framework (COF) photocatalyst, FS‐OHOMe‐COF. The FS‐OHOMe‐COF structure features extended π–π conjugation that improves charge mobility, while the introduction of sulfone units not only as active sites facilitates surface reactions with water but also bolsters stability through increased interlayer forces. The resulting FS‐OHOMe‐COF has a low exciton binding energy, long excited‐state lifetime and high photo‐stability that leads to high performance for photocatalytic H2O2 production (up to 1.0 mM h−1) with an H2O2 output of 19 mM after 72 hours of irradiation. Furthermore, the catalyst demonstrates high stability, which sustained activity over 192 hours of photocatalytic experiment.

Therapeutic Peptides, Proteins and their Nanostructures for Drug Delivery and Precision Medicine.

Peptide and protein nanostructures with tunable structural features, multifunctionality, biocompatibility and biomolecular recognition capacity enable development of efficient targeted drug delivery tools for precision medicine applications. In this review article, we present various techniques employed for the synthesis and self-assembly of peptides and proteins into nanostructures. We discuss design strategies utilized to enhance their stability, drug-loading capacity, and controlled release properties, in addition to the mechanisms by which peptide nanostructures interact with target cells, including receptor-mediated endocytosis and cell-penetrating capabilities. We also explore the potential of peptide and protein nanostructures for precision medicine, focusing on applications in personalized therapies and disease-specific targeting for diagnostics and therapeutics in diseases such as cancer.

Predicting the tensile and compressive modulus of electrospun fiber mat‐reinforced hydrogels using the Halpin–Tsai equations

AbstractThe reinforcement of mechanically‐weak hydrogels to yield composites with increased stiffness, strength, or toughness is a well‐established approach. In particular, introducing electrospun nanofibers into hydrogels is a common strategy for biomedical applications, as the resulting hierarchical structure mimics biology and allows for control over fiber diameter and alignment and tuning of mechanical properties. However, further study of the link between the constituent materials and the mechanical properties of the composite is uncommon. One potential model to understand the mechanical properties of fiber‐reinforced hydrogels involves the Halpin–Tsai equations, which relate the modulus values of the fibers and hydrogel matrix and the fiber volume fraction, to the modulus of the composite. To assess the application of this model to fiber‐reinforced hydrogels, predicted values were compared with experimental values from mechanical testing of a poly(ethylene glycol) (PEG) matrix reinforced with an electrospun polycaprolactone (PCL) fiber mat. Although the equations described these systems well in tension, providing a facile approach to identify a fiber volume fraction that will achieve a desired modulus, the Halpin–Tsai approach was less successful under compression. This study motivates additional investigation of the role of structural features of hydrogel composites in determining mechanical properties to enable design of materials for specific applications.

Colloidal Photonic Composites with a Long-Range Order by Hot-Pressing Polymer Brush-Grafted Silica Colloids.

Colloidal photonic composites (CPCs) are unique optical materials that combine flexible and responsive polymers with colloidal photonic crystals, and they have promising applications in colorful displays, optical anticounterfeiting, and visual sensors. However, conventional self-assembly strategies for constructing CPCs via solvent evaporation have faced limitations due to the meticulous regulation required during the evaporation process and typically long preparation durations. Here, we present an external force method to achieve a long-range ordered arrangement in CPCs by hot-pressing poly(2-[[(butylamino)carbonyl]oxy]ethyl acrylate (PBCOE)) brush-grafted silica colloidal particles (SiO2-g-PBCOE). We show that the hot-pressing conditions (i.e., temperature and pressure) and the silica volume fraction (φsilica) of the SiO2-g-PBCOE colloidal particles play crucial roles in determining their ordering and optical properties. By optimization of the hot-pressing temperature up to 100 °C and pressure of 5 MPa, a long-range ordered arrangement of SiO2-g-PBCOE colloidal particles with a φsilica of 20.3% can be achieved. For the effect of structural features, our findings reveal that SiO2-g-PBCOE colloidal particles featuring a higher φsilica are more prone to obtain a long-range ordered arrangement compared to a lower φsilica under hot-pressing conditions at relatively low temperature and pressure (50 °C and 5 MPa), which is mainly attributed to the chain entanglement and hydrogen bonding interactions induced by grafted longer polymer brushes, leading to additional energy inputs and weakening the ordering. Significantly, the critical φsilica (φc) of SiO2-g-PBCOE colloidal particles is discerned, strongly influencing the optical properties of the hot-pressed films. Specifically, a hot-pressed SiO2-g-PBCOE film with a critical φsilica of 29.3% displays enhanced optical properties characterized by intensified reflection peaks, narrowed full width at half-maximum (FWHM), and brilliant structural colors. Notably, in this work, we reveal the mechanism of hot-pressing-driven core-shell colloidal particle ordering and the key factors affecting the ordering of colloidal particles, i.e., chain entanglement and hydrogen-bonding interactions, which play a crucial role in obtaining CPCs with controllable structures. Moreover, angle-dependent structural color is observed in the hot-pressed SiO2-g-PBCOE film with a φsilica content of 29.3% due to the unique attributes of the highly ordered arrangement, while the films exhibit mechanochromic properties due to chain entanglement and hydrogen bonding interactions. This work provides valuable insights into the rapid construction of highly ordered CPCs and establishes a solid foundation for external force-assisted ordering of colloidal particles.

Prediction of Metal-Organic Frameworks with Phase Transition via Machine Learning.

Metal-organic frameworks (MOFs) possess a virtually unlimited number of potential structures. Although the latter enables an efficient route to control the structure-related functional properties of MOFs, it still complicates the prediction and searching for an optimal structure for specific application. Next to prediction of the MOFs for gas sorption/separation and catalysis via machine learning (ML), we report on ML to find MOFs demonstrating a phase transition (PT). On the basis of an available QMOF database (7463 frameworks), we create and train the autoencoder followed by training the classifier of MOFs from a unique database with experimentally confirmed PT. This makes it possible to identify MOFs with a high potential for PT and evaluate the most likely stimulus for it (guest molecules or temperature/pressure). The formed list of available MOFs for PT allows us to discuss their structural features and opens an opportunity to search for phase change MOFs for diverse physical/chemical application.

Agencement of onlife and phygital: smart tech–enabled value co-creation practices

PurposeIn this article, we reflect on how smart technology is transforming service research discourses about service innovation and value co-creation. We adopt the concept of technology smartness’ to refer to the ability of technology to sense, adapt and learn from interactions. Accordingly, we seek to address how smart technologies (i.e. cognitive and distributed technology) can be powerful resources, capable of innovating in relation to actors’ agency, the structure of the service ecosystem and value co-creation practices.Design/methodology/approachThis conceptual article integrates evidence from the existing theories with illustrative examples to advance research on service innovation and value co-creation.FindingsThrough the performative utterances of new tech words, such as onlife and materiality, this article identifies the emergence of innovative forms of agency and structure. Onlife agency entails automated, relational and performative forms, which provide for new decision-making capabilities and expanded opportunities to co-create value. Phygital materiality pertains to new structural features, comprised of new resources and contexts that have distinctive intelligence, autonomy and performativity. The dialectic between onlife agency and phygital materiality (structure) lies in the agencement of smart tech–enabled value co-creation practices based on the notion of becoming that involves not only resources but also actors and contexts.Originality/valueThis paper proposes a novel conceptual framework that advances a tech-based ecology for service ecosystems, in which value co-creation is enacted by the smartness of technology, which emerges through systemic and performative intra-actions between actors (onlife agency), resources and contexts (phygital materiality and structure).

Topology Controlled All-(Meth)acrylic Thermoplastic Elastomers by Multi-Functional Lewis Pairs-Mediated Polymerization.

It remains challenging to synthesize all-(meth)acrylic triblock thermoplastic elastomers (TPEs), due to the drastically different reactivities between the acrylates and methacrylates and inevitable occurrence of side reactions during polymerization of acrylates. By taking advantage of the easy structural modulation features of N-heterocyclic olefins (NHOs), we design and synthesize strong nucleophilic tetraphenylethylene-based NHOs varying in the number (i.e. mono-, dual- and tetra-) of initiating functional groups. Its combination with bulky organoaluminum [iBuAl(BHT)2] (BHT=bis(2,6-di-tBu-4-methylphenoxy)) constructs Lewis pair (LP) to realize the living polymerization of both acrylates and methacrylates, furnishing polyacrylates with ultrahigh molecular weight (Mn up to 2174 kg ⋅ mol-1) within 4 min. Moreover, these NHO-based LPs enable us to not only realize the control over the polymers' topology (i.e. linear and star), but also achieve triblock star copolymers in one-step manner. Mechanical studies reveal that the star triblock TPEs exhibit better mechanical properties (elongation at break up to 1863 % and tensile strength up to 19.1 MPa) in comparison with the linear analogs. Moreover, the presence of tetraphenylethylene group in the NHOs entitled the triblock TPEs with excellent AIE properties in both solution and solid state.