Grid-like Structure Research Articles

Evolution of the periphery of a self-organized road network

Urban roads are geographical planar networks, making their growth patterns constrained over the available physical space. In this work, we quantify the center (core)-periphery structure of an urban road system based on the betweenness centrality of the nodes, and then measure their spread over the evolution of the urban zone. Using corrected and georeferenced historical maps of Manila, Philippines, we observe temporal invariance of the betweenness centrality distributions, along with the preponderance of comparable-betweenness node connections, both of which signify the preservation of the planarity of the network. Despite the geographical expansion of the road network, we observe topologically peripheral nodes in the geographical center for all the map periods considered, signifying the presence and growth of dendritic dead-ends which may ultimately affect the traffic flow. The shortest paths among the topological periphery shows an evolution towards a homogenized, grid-like structure for the entire network. Understanding peripheral connections, not only for planar networks but also for other complex networked entities, may hint at the efficiency of transport within the system, as these peripheral nodes represent the least “important” sites in the network.

Application of 3D-printed tissue-engineered skin substitute using innovative biomaterial loaded with human adipose-derived stem cells in wound healing.

Large-scale skin injuries are usually accompanied by impaired wound healing, resulting in scar formation, or significant morbidity and mortality. The aim of this study is to explore the in vivo application of 3D-printed tissue-engineered skin substitute using innovative biomaterial loaded with human adipose-derived stem cells (hADSCs) in wound healing. Adipose tissue was decellularized, and extracellular matrix components were lyophilized and solubilized to obtain adipose tissue decellularized extracellular matrix (dECM) pre-gel. The newly designed biomaterial is composed of adipose tissue dECM pre-gel, methacrylated gelatin (GelMA), and methacrylated hyaluronic acid (HAMA). Rheological measurement was performed to evaluate the phase-transition temperature and the storage and loss modulus at this temperature. Tissue-engineered skin substitute loaded with hADSCs was fabricated by 3D printing. We used nude mice to establish full-thickness skin wound healing model and divided them into four groups randomly: (A) Full-thickness skin graft treatment group, (B) 3D-bioprinted skin substitute treatment group as the experimental group, (C) microskin graft treatment group, and (D) control group. The amount of DNA in each milligram of dECM was 24.5 ± 7.1 ng, fulfilling the currently accepted decellularization criteria. The solubilized adipose tissue dECM was thermo-sensitive biomaterial and underwent a sol-gel phase transition when temperature rises. The dECM-GelMA-HAMA precursor undergoes a gel-sol phase transition at 17.5°C, where the storage and loss modulus of the precursor is about 8 Pa. The scanning electron microscope showed that the interior of crosslinked dECM-GelMA-HAMA hydrogel is 3D porous network structure with suitable porosity and pore size. The shape of the skin substitute is stable with regular grid-like scaffold structure. Wound healing in the experimented animals was accelerated after being treated with 3D-printed skin substitute, which attenuate inflammatory response, increase blood perfusion around the wound, as well as promote re-epithelialization, collagen deposition and alignment, and angiogenesis. In summary, 3D-printed dECM-GelMA-HAMA tissue-engineered skin substitute loaded with hADSCs, which can be fabricated by 3D printing, can accelerate wound healing and improve healing quality by promoting angiogenesis. The hADSCs and the stable 3D-printed stereoscopic grid-like scaffold structure play a critical role in promoting wound healing.

Designing Bioinspired Composite Structures via Genetic Algorithm and Conditional Variational Autoencoder

Designing composite materials with tailored stiffness and toughness is challenging due to the massive number of possible material and geometry combinations. Although various studies have applied machine learning techniques and optimization methods to tackle this problem, we still lack a complete understanding of the material effects at different positions and a systematic experimental procedure to validate the results. Here we study a two-dimensional (2D) binary composite system with an edge crack and grid-like structure using a Genetic Algorithm (GA) and Conditional Variational Autoencoder (CVAE), which can design a composite with desired stiffness and toughness. The fitness of each design is evaluated using the negative mean square error of their predicted stiffness and toughness and the target values. We use finite element simulations to generate a machine-learning dataset and perform tensile tests on 3D-printed specimens to validate our results. We show that adding soft material behind the crack tip, instead of ahead of the tip, tremendously increases the overall toughness of the composite. We also show that while GA generates composite designs with slightly better accuracy (both methods perform well, with errors below 20%), CVAE takes considerably less time (~1/7500) to generate designs. Our findings may provide insights into the effect of adding soft material at different locations of a composite system and may also provide guidelines for conducting experiments and Explainable Artificial Intelligence (XAI) to validate the results.

Improvement of Surface Quality for Titanium Alloys During Counter-Rotating Electrochemical Machining Using an Auxiliary Cathode

Counter-rotating electrochemical machining (CRECM) is an innovative ECM method that can be used to manufacture revolving parts, especially titanium alloy casings. Because titanium alloys are easily passivated metals, sufficient electrical quantity is required to achieve the transition from pitting to smoothing with a lower speed. However, a low rotational speed means more material is removed in a machining cycle, which is not conducive to wall thickness control. Therefore, achieving fast smoothing at a relatively high rotational speed for titanium alloys is focused. In this paper, the proposed methodology applies an auxiliary cathode to accelerate the transition from pitting corrosion to smoothness of titanium alloys at a relatively high rotational speed. Experimental results show that titanium alloy can be smoothed regardless of rotational speed with an auxiliary cathode. The auxiliary cathode can weaken the off-current density region and accelerate the breakdown of the oxide layer, and the layer-by-layer dissolution of micron-thick materials is achieved at a relatively high rotational speed. The grid-like convex structure is produced at a relatively high rotational speed with an auxiliary cathode, and the surface roughness is 1.808 μm. This fully demonstrates the reliability of the auxiliary cathode for achieving the smoothness of titanium alloys.

An innovative additively manufactured implant for mandibular injuries: Design and preparation processes based on simulation model.

Objective: For mandibular injury, how to utilize 3D implants with novel structures to promote the reconstruction of large mandibular bone defect is the major focus of clinical and basic research. This study proposed a novel 3D titanium lattice-like implant for mandibular injuries based on simulation model, which is designed and optimized by a biomechanical/mechanobiological approach, and the working framework for optimal design and preparation processes of the implant has been validated to tailored to specific patient biomechanical, physiological and clinical requirements. Methods: This objective has been achieved by matching and assembling different morphologies of a lattice-like implant mimicking cancellous and cortical bone morphologies and properties, namely, an internal spongy trabecular-like structure that can be filled with bone graft materials and an external grid-like structure that can ensure the mechanical bearing capacity. Finite element analysis has been applied to evaluate the stress/strain distribution of the implant and bone graft materials under physiological loading conditions to determine whether and where the implant needs to be optimized. A topological optimization approach was employed to improve biomechanical and mechanobiological properties by adjusting the overall/local structural design of the implant. Results: The computational results demonstrated that, on average, values of the maximum von-Mises stress in the implant model nodes could be decreased by 43.14% and that the percentage of optimal physiological strains in the bone graft materials can be increased from 35.79 to 93.36% since early regeneration stages. Metal additive manufacturing technology was adopted to prepare the 3D lattice-like implant to verify its feasibility for fabrication. Following the working framework proposed in this study, the well-designed customized implants have both excellent biomechanical and mechanobiological properties, avoiding mechanical failure and providing sufficient biomechanical stimuli to promote new bone regeneration. Conclusion: This study is expected to provide a scientific and feasible clinical strategy for repairing large injuries of mandibular bone defects by offering new insights into design criteria for regenerative implants.

Successful 2D MoS2 nanosheets synthesis with SnSe grid-like nanoparticles: Photoelectrochemical hydrogen generation and solar cell applications

Preparing a two-dimensional (2D) lateral heterostructure film with n and p-type materials in the same film with high control distribution remains challenging so far. In this paper, a lateral heterostructure film based on 2D MoS2 nanosheets (NSs) with SnSe nanoparticles (NPs) “ SnSe/MoS2 ” is prepared via a facile one-step electrophoretic deposition method. MoS2 NSs are stacked vertically to form a film while SnSe NPs are incorporated to form nanoparticles of geometric grid-like inside the film. The film structure was confirmed through a scanning electron microscope. Such a heterostructure improves the performance of PEC hydrogen generation due to the fast separation of electron-hole pairs and hindering recombination. The short-circuit current density (JSC), as well as the open circuit voltage (VOC) of heterostructure film, reaches 1.75 mA cm−2 and 0.17 V, respectively, under a visible light illumination of 100 mW cm-2. The produced (Jph) values increase from 0.52 to 1.75 mA cm−2 under a light intensity ranging from 10 to 100 mW.cm−2, respectively. The grid-like structure of the SnSe/MoS2 heterostructure advantages its application for solar cells owing to the important potential difference that ensures the formation of p and n-type materials in the same film. This is the aim of our future work, where a complete synthesis of solar cells with high efficiency will be given, in which the p and n types of materials will face the sunlight directly.

Effect of Penetrative Combustion on Regression Rate of 3D Printed Hybrid Rocket Fuel

3D printing manufacturing is used to manufacture hybrid rocket fuel grains featuring a special grid-like structure in order to control combustion performance. An innovative penetrative combustion mechanism, capable of affecting regression rate, was noticed during the combustion of low-packing density grains. The 3D printing manufacture was implemented using acrylonitrile-butadiene-styrene (ABS) material to clarify this mechanism and the corresponding combustion performance. Grid-like structure fuels with different packing densities were prepared to assess the effects of penetrative combustion on fuel combustion performance. The thermal decomposition of ABS was analyzed by infra-red spectroscopic analysis (FTIR) and thermogravimetric analysis-differential thermal scanning (TG-DSC). The internal structure of the ABS grains was observed by high-resolution 3D micro-computed tomography (μCT). All fuel grains were burned in a hybrid 2D radial burner, allowing visualization of the combustion process and evaluation of the ballistic parameters. The experimental results suggest that the combustion process of the ABS porous grains includes two regimes, both featuring an increased regression rate. In the normal layer-by-layer burning regime, at Gox=45 kg/(m2·s), the regression rates of 100% and 90% ABS increased by 29.6% and 38.1%, respectively, compared with solid ABS which was manufactured by a computerized numerical control (CNC) lathe. In the fracture-led volumetric burning regime, data acquisition is more difficult, but the regression rate is again observed to increase as the packing density decreases.

3D spiderweb-like nanowire networks loading single-atom catalysts in capillary array as high-efficiency microreactor

Rapid fluid transport and high reaction efficiency are known as irreconcilable indicators for designing high-performance monolithic microreactors. Here, proceeding from microstructure design and single-atom catalysts (SACs) utilization simultaneously, we developed 3D spiderweb-like nanowire networks in wood capillary array owning high permeability integrated with successfully synthesized SACs as high-efficiency microreactor for organic pollutant removal from water. The 3D spiderweb-like nanowire networks could be obtained by noncovalent and physical entanglement interactions of ultralong and flexible PEG113-b-P4VP104 worm-like micelles forming loosened micro-aerogel in the wood capillary. Abundant nanowire networks in straight microchannels array allow vast quantities of catalyst active component loading sites and greatly increase contact reaction efficiency. Meanwhile, the grid-like porous structure of the nanowire networks preferably maintains good permeability of the wood capillary array. Furthermore, strong coordination interaction of pyridine in P4VP block and confinement effect from core-shell structured worm-like micelles afford uniformly distributed SACs with high density. As a proof of concept, we demonstrate an integration of unique microstructure from 3D spiderweb-like nanowire networks in wood capillary array and well dispersed Pd-N4/Fe-N4 SACs enables high removal efficiency for methylene blue and fast mass transfer for water. Importantly, this work provides a facile and scalable method for atomically dispersed catalysts, paving the way for designing high-performance microreactors in other potentially important reactions.

Free as a Bird, but at What Cost?

As-the-crow-flies (ATCF) navigation is an alternative to turn-by-turn navigation for cyclists utilizing the least-angle strategy by providing the beeline to the destination. However, past research found weaknesses (e.g., running into dead ends) affecting the user experience. In this paper, we investigate how the street network attributes to the experience of the navigation method. Using a feature importance analysis and comparison of different city types across 1633 cities, we analyze how ATCF navigation fits different environments. The perfect ACTF-city has long streets, many options to turn at decision points, few dead ends, and a grid-like structure. Cities well suited are primarily found in East Asia and North America. Furthermore, we find that previous ATCF studies were most likely conducted in Western Europe, which features the least suited street networks for the navigation method. We present design implications for future ATCF implementations and argue for diverse study locations in future research.

GCN-FFNN: A two-stream deep model for learning solution to partial differential equations

This paper introduces a novel two-stream deep model based on graph convolutional network (GCN) architecture and feed-forward neural networks (FFNN) for learning the solution of nonlinear partial differential equations (PDEs). The model aims at incorporating both graph and grid input representations using two streams corresponding to GCN and FFNN models, respectively. Each stream layer receives and processes its input representation. As opposed to FFNN which receives a grid-like structure, the GCN stream layer operates on graph input data where the neighborhood information is incorporated through the adjacency matrix of the graph. In this way, the proposed GCN-FFNN model learns from two types of input representations, i.e. grid and graph data, obtained via the discretization of the PDE domain. The GCN-FFNN model is trained in two phases. In the first phase, the model parameters of each stream are trained separately. Both streams employ the same error function to adjust their parameters by enforcing the models to satisfy the given PDE as well as its initial and boundary conditions on grid or graph collocation (training) data. In the second phase, the learned parameters of two-stream layers are frozen and their learned representation solutions are fed to fully connected layers whose parameters are learned using the previously used error function. The learned GCN-FFNN model is tested on test data located both inside and outside the PDE domain. The obtained numerical results demonstrate the applicability and efficiency of the proposed GCN-FFNN model over individual GCN and FFNN models on 1D-Burgers, 1D-Schrödinger, 2D-Burgers, and 2D-Schrödinger equations.

Hagrid: using Hilbert and Gosper curves to gridify scatterplots

A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid, a technique that uses space-filling curves (SFCs) to “gridify” a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid, Small multiples with gaps SMWG, and CorrelatedMultiples CMDS, in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot.Graphical abstract

Optimized vertical phase separation via systematic Y6 inner side-chain modulation for non-halogen solvent processed inverted organic solar cells

Organic solar cells (OSCs) have entered a new phase of development with the advent of a high-performance non-fullerene acceptor known as Y6. Considering that the molecular curvature shape of Y6 allows for dense intermolecular packing with a grid-like packing structure, the modification of branched alkyl chains located on the pyrrole motif of Y6 is expected to have a significant impact on morphological properties, such as molecular packing/orientation behavior and vertical phase separation. In this work, we fine-tune the branched alkyl chain attached to nitrogen atoms of the Y6 main backbone and systematically investigate the relationship between the morphological features and photovoltaic performance of OSCs with a PM6 donor polymer. The systematic alkyl chain engineering effectively reduces the aggregation of Y6-analogs and substantially improves solubility, thereby providing solution processability in a non-halogenated solvent. Additional in-depth analyses reveal that the modification of branched alkyl chains allows the formation of an optimized bulk heterojunction morphology, which is advantageous for an inverted device architecture. In particular, the PM6:Y6-HU-based inverted OSC utilizing o-xylene as a processing solvent achieves a power conversion efficiency of 17.4% with an outstanding fill factor of 77.9 % under the binary blend system and single non-halogen solvent processing.

Formation and growth mechanism of Strontium hydroxyapatite cellular crystal sphere

Strontium hydroxyapatites (SrHAps) find widespread applications in bone tissue engineering and drug carrier material for bone disease treatment because of biocompatibility. Morphology controlled preparation of SrHAps and understanding of the synthesis mechanism are critical to rationalize design hierarchically structured SrHAps and expand their applications. Herein, SrHAp cellular crystal spheres have been synthesized by using (NH4)3C6H5O7– induced (citrate-induced) homogeneous precipitation method under ambient pressure. Morphology characterization has shown that the as-obtained SrHAp is the secondary particle, while the spherical structure constituted the primary cellular crystal. Driving force of Sr2+ concentration gradient in phase transition has been analyzed to explain the synthesis mechanism of unique hierarchically structured SrHAp cellular crystals. The formation of SrHAp cellular crystal spheres can be attributed to the strong coordination within C6H5O73– and Sr2+ to form a 3D grid-like structure. This research has been able to overcome the longstanding limitations of traditional synthesis methods to establish the efficient route for synthesizing SrHAp with special morphology and predictable and controllable hierarchically structured SrHAp.

Improvement on the machining accuracy of titanium alloy casing during counter-rotating electrochemical machining by using an insulation coating

Counter-rotating electrochemical machining (CRECM) is an innovative ECM method that can be used to manufacture revolving parts, especially thin-walled casings with complex convex structures. As titanium alloys are easy to passivate and difficult to machine, active NaCl solution is often used to obtain a good surface quality from ECM. However, the top surfaces of convex structures will be subjected to severe over cutting using NaCl solution, resulting in poor machining accuracy. Therefore, an insulation coating is employed to protect the non-processed area by shielding stray currents in this paper. A simulation model is established to investigate the evolution of convex structures with an insulation coating. According to the simulated convex structures, the top of the convex structure suffers from serious stray corrosion without the protection of insulation coating, and the sidewall inclination angle is 25.42°. However, the insulation coating enables convex structures without over cutting, achieving a sidewall inclination angle of only approximately 1.1°. Under the protection of insulation coating, the width and height decrease linearly with time, and the radius of the root fillet increases towards an approximately constant value. The experimental results indicate that stray currents can be shielded completely with an insulation coating, and no electrochemical dissolution occurs on the top of the convex structure. The deviation between simulation and experimental results does not exceed 10 %, demonstrating reliability of the proposed model. Cylindrical and conical parts with a grid-like convex structure are successfully produced with a surface roughness value of Ra1.8 μm. This fully verifies the effectiveness of an insulation coating and demonstrates the excellent processing capability of CRECM.

RECYCLED PLASTIC COMPOSITES IMPREGNATED WITH ORGANOCLAY AS POTENTIAL GEOGRID REINFORCEMENT MATERIAL FOR PAVEMENT APPLICATION

Geogrid in pavement application is widely used today, as it mainly functions as soil reinforcement or as a separator. Geogrid is made up of polymers which makes it resistant to degradation. There are different kinds of polymers which are widely used today in a familiar term called plastics. For the past years, plastic aluminum laminates (PAL) and high-density polyethylene (HDPE) plastic bags were used as packaging materials or containers. However, the use of these plastics generates a huge amount of waste, consuming a lot of space and damaging the natural state of the earth. This research aims to utilize these wastes to form geogrid, which will be applied in pavement design. The recycled plastics undergo the melting process, with the aid of the two-roll mill and the compression molding. Then apertures are carved into a grid-like structure. A total of nine mixtures of geogrids were produced, each mixture having a different composition of HDPE, PAL, and organoclay. Tensile test for geogrids by Multi-rib Tensile Method (ASTM 6637) and tensile test for plastics (ASTM D638) are followed to test the geogrids. Based on the data obtained, it showed that higher tensile strength is achieved when the amount of HDPE composition in the material increases. In terms of the addition of organoclay, the mixture with 98% HDPE and 2% organoclay produced the highest tensile strength. The product of the study exhibiting the highest strength was classified as either class 2 or class 3 geogrid.

Effect of CO–CO2 and H2–H2O on the reduction degree of fluxed pellets: reduction mechanism within hydrogen-rich blast furnace

ABSTRACT In order to explore the mechanism of hydrogen-rich blast furnace on improving the reduction behaviour of fluxed pellets, the isothermal reduction experiments of fluxed pellets in CO-CO2-N2 and CO-CO2-H2-H2O-N2 atmosphere were studied. The results show that the condition of hydrogen-rich blast furnace at 900°C has a significant effect on improving the reduction degree of fluxed pellets. It is helpful to increase the reduction rate of fluxed pellets at 1000~1100°C. The phenomenon of "reduction stagnation" at 800°C is due to the metallic iron layer formed on the periphery of the fluxed pellets, which hinders the entry of reducing gas. The morphological observation of the reduction products showed that the grid-like metallic iron structure is the key factor to improve the reduction rate and reduction degree of fluxed pellets in CO-CO2-H2-H2O-N2. Finally, the reduction mechanism of flux particles in hydrogen-rich blast furnace is summarized.

Bridging the gap between one-to-many and one-to-one label assignment via NMS-aware alignment module

The recently proposed one-to-one label assignment rules have made it possible for the detector to get rid of its dependence on NMS. However, its performance is inferior to conventional one-to-many label assignment trained detector due to insufficient supervision caused by reduced foreground information. In this paper, to combine the advantages of these two kinds of label assignment rules, we assign two sets of positive samples by one-to-many and one-to-one label assignment rules respectively to train the detector. Furthermore, we introduce the graph convolutional network (GCN) to construct a NMS-aware alignment module. The NMS-aware alignment module is devised to bridge the gap between the two sets of positive samples and generate a mask to select out best detections, which plays the role of NMS. It also gets rid of the limitation of processing the grid-like data structure and effectively aggregates information from the duplicate predictions. Extensive experiments on the COCO dataset show the effectiveness of our method and the proposed method achieves competitive performance against the recent NMS-free detectors. Especially, our proposed method is compatible with both anchor-based and anchor-free or one-stage and two-stage architectures. The code will be made public soon.

Guest Editorial: Non-Euclidean Machine Learning

Over the past decade, deep learning has had a revolutionary impact on a broad range of fields such as computer vision and image processing, computational photography, medical imaging and speech and language analysis and synthesis etc. Deep learning technologies are estimated to have added billions in business value, created new markets, and transformed entire industrial segments. Most of today’s successful deep learning methods such as Convolutional Neural Networks (CNNs) rely on classical signal processing models that limit their applicability to data with underlying Euclidean grid-like structure, e.g., images or acoustic signals. Yet, many applications deal with non-Euclidean (graph- or manifold-structured) data. For example, in social network analysis the users and their attributes are generally modeled as signals on the vertices of graphs. In biology protein-to-protein interactions are modeled as graphs. In computer vision & graphics 3D objects are modeled as meshes or point clouds. Furthermore, a graph representation is a very natural way to describe interactions between objects or signals. The classical deep learning paradigm on Euclidean domains falls short in providing appropriate tools for such kind of data. Until recently, the lack of deep learning models capable of correctly dealing with non-Euclidean data has been a major obstacle in these fields. This special section addresses the need to bring together leading efforts in non-Euclidean deep learning across all communities. From the papers that the special received twelve were selected for publication. The selected papers can naturally fall in three distinct categories: (a) methodologies that advance machine learning on data that are represented as graphs, (b) methodologies that advance machine learning on manifold-valued data, and (c) applications of machine learning methodologies on non-Euclidean spaces in computer vision and medical imaging. We briefly review the accepted papers in each of the groups.

Cyclodextrin-Templated Co(II) Grids: Symmetry Control over Supramolecular Topology and Magnetic Properties.

While inherent complexation properties and propensity for self-organization of cyclodextrins (CDs) render them potentially promising scaffolds of magnetic materials, this research area is still at an embryonic stage. We report on the synthesis and structure characterization of a new sandwich-type complex, [(α-CD)2Co3Li6(H2O)9] (α-1), which represents a smaller analogue of the previously characterized [(γ-CD)2Co4Li8(H2O)12] (γ-1) cluster. A comprehensive structural analysis of α-1 and a careful reinvestigation of γ-1 reveal how the symmetry of CD ligands determines the molecular composition and supramolecular arrangements of Co/Li sandwich-type complexes. Furthermore, the first comparative studies of the magnetic properties in this type of system point to subtle differences in the magnetic behavior of both compounds. The sandwich-type complexes α-1 and γ-1 exhibit field-induced slow magnetic relaxation, defining a new family of magnetic materials with a pillared grid-like supramolecular structure composed of weakly interacting CoII centers forming an SMM.

Near-Infrared multifunctional theranostic agent with Wave-Like aggregates modulated by substituent position effect

Precise design of organic photosensitizers (PSs) promoted the technological innovation for multimodal imaging-guided synergistic therapy. Nonetheless, various group substitution could not only optimize the basic photophysical behavior, but possibly change the aggregate, which handicaps the deep understanding of the “Formula-Aggergete-Property” relationship. Bearing this in mind, herein two isomers, named 6-TDE and 7-TDE, were prepared via substituting position modification. Among them, 6-TDE exhibited the grid-like structure, while 7-TDE presented wavy-like structure. Despite the aggregates were different, 6-TDE and 7-TDE shared common features including partly twisted backbone and non-overlapped-orbit, hence resulting in similar optical physical behavior such as decent extinction coefficient, near-IR emission, large stockes shifts, etc. Meanwhile, though two PSs could both generated Type-I and Type-II ROS, 7-TDE possessed smaller singlet–triplet splitting (ΔEST), which exhibited favorable ROS as well as outstanding mitochondrial targeting, achieving efficient photodynamic therapy (PDT) effect. During this process, mitochondrial autophagy could be tracked and observed effectively and in real-time. Moreover, 7-TDE presented outstanding performance in multimodal imaging, including fluorescence imaging (FLI), photoacousticimaging (PAI) and photothermal imaging (PTI). This study enriches the strategy of precise molecular engineering to optimize theranostic agents.