Multi-scale Structural Features Research Articles

A data-driven microstructure-based model for predicting circumferential behavior and failure in degenerated human annulus fibrosus

The degeneration of the intervertebral disc annulus fibrosus poses significant challenges in understanding and predicting its mechanical behavior. In this article, we present a novel approach, enriched with detailed insights into microstructure and degeneration progression, to accurately predict the mechanics of the degenerated human annulus. Central to this framework is a fully three-dimensional continuum-based model that integrates hydration state and multiscale structural features, including proteoglycan macromolecules and interpenetrating collagen fibrillar networks across various hierarchical levels within the multi-layered lamellar/inter-lamellar soft tissue, capable of sustaining deformation-induced damage. To ensure accurate and comprehensive predictions of the degenerated annulus mechanical behavior, we establish a data-driven correlation between disc degeneration grade and individual age, which influences the composition and mechanical integrity of annulus constituents while accounting for regional variations. The methodology includes a thorough identification of age- and grade-related evolutions of model inputs, followed by a detailed quantitative evaluation of the model predictive capabilities, with a focus on circumferential behavior and failure. The model successfully replicates experimental data, accurately capturing stiffness, transverse response (Poisson's ratio), and ultimate properties across different annulus regions, while also accommodating the modulation of the age/grade relationship. The reduction rates between normal and severe degeneration align reasonably well with experimental data, with the inner region exhibiting the largest decrease in stiffness (34.63%) and no significant change observed in the outer region. Failure stress drops considerably in both regions (49.86% in the inner and 45.33% in the outer), while failure strain decreases by 36.39% in the outer and 24.74% in the inner. Our findings demonstrate that the proposed framework significantly enhances the predictive accuracy of annulus mechanics across a spectrum of degeneration levels, from normal to severely degenerated states. This approach promises improved predictive accuracy, deeper insights into disc health and injury risk, and a robust foundation for further research on the impact of degeneration on disc integrity. Statement of SignificanceUnderstanding and predicting the mechanical behavior of degenerated human annulus fibrosus remains a significant challenge due to the complex interplay of structural, biochemical, and age-related factors. This study presents a microstructure-based approach to address this challenge by integrating hydration state, detailed structural features across hierarchical scales, and deformation-induced damage and failure, alongside age-related changes and degeneration grade factors. This approach enables accurate simulations of annulus mechanics across regions, with model results thoroughly compared to available data, reinforcing its applicability in capturing degeneration effects. By capturing the intricate interactions between microstructure and mechanical behavior in degenerated discs, the model lays a strong foundation for improving clinical assessments and guiding future treatment strategies for disc-related conditions.

Vascular bundle-structured polymeric composites with fire-safe, self-detecting and heat warning capabilities for power batteries thermal management

The trend of miniaturization and integration poses challenges to the thermal management of electronic devices, requiring high thermal conductivity and potential fire safety, etc. In this study, inspired by plant vascular structure, we developed a polymer composite with a vertical vascular bundle structure via a sacrificial template method and subsequent assembly of transition metal carbides/nitrides (MXene) nanosheets and phytic acid (PA) coordinated cobalt ions (Co2+) complex. The embedded MXene and PA@Co exhibit multilayer multiscale structural features, forming heat transfer channels and protective cells within the composite. The resultant composites possess high out-of-plane thermal conductivity (∼1.54 W‧m−1‧k−1) and excellent flame retardancy, including self-extinguishing, and significantly reduced heat and smoke release. Interestingly, the MXene vascular bundle structure imparts heat early warning capabilities and intelligent damage self-detection, suggesting an effective means of preventing early-stage fires and real-time monitoring of composite structural and functional integrity. Such biomimetic strategies enable new insights into the designing of multifunctional, intelligent polymer composites.

GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features.

Recent advances in high-throughput sequencing have led to an explosion of genomic and transcriptomic data, offering a wealth of protein sequence information. However, the functions of most proteins remain unannotated. Traditional experimental methods for annotation of protein functions are costly and time-consuming. Current deep learning methods typically rely on Graph Convolutional Networks to propagate features between protein residues. However, these methods fail to capture fine atomic-level geometric structural features and cannot directly compute or propagate structural features (such as distances, directions, and angles) when transmitting features, often simplifying them to scalars. Additionally, difficulties in capturing long-range dependencies limit the model's ability to identify key nodes (residues). To address these challenges, we propose a geometric graph network (GGN-GO) for predicting protein function that enriches feature extraction by capturing multi-scale geometric structural features at the atomic and residue levels. We use a geometric vector perceptron to convert these features into vector representations and aggregate them with node features for better understanding and propagation in the network. Moreover, we introduce a graph attention pooling layer captures key node information by adaptively aggregating local functional motifs, while contrastive learning enhances graph representation discriminability through random noise and different views. The experimental results show that GGN-GO outperforms six comparative methods in tasks with the most labels for both experimentally validated and predicted protein structures. Furthermore, GGN-GO identifies functional residues corresponding to those experimentally confirmed, showcasing its interpretability and the ability to pinpoint key protein regions. The code and data are available at: https://github.com/MiJia-ID/GGN-GO.

Emulsion-Based Multiscale Structural Design Realizes Lightweight and Superelastic Graphene Aerogels for Electromagnetic Interference Shielding.

Ultralight graphene aerogels with high electrical conductivity and superelasticity are demanded yet difficult to produce. A versatile emulsion-based approach is demonstrate to optimize multiscale structure of lightweight, elastic, and conductive graphene aerogels. By constructing Pickering emulsion using graphene oxide (GO), poly (amic acid) (PAA), and octadeyl amine (ODA), micron-level close-pore structure is realized while thermal shrinkage mismatch between GO and PAA creates numerous nanowrinkles during thermal annealing. GO nanosheets are bridged by PAA-derived carbon, enhancing the structural integrity at molecular level. These multiscale structural features facilitate rapid electron transport and efficient load transfer, conferring graphene aerogels with intriguing mechanical and electromagnetic interference (EMI) shielding properties. The emulsion-based graphene aerogel with an ultralow density of ≈3.0mg cm-3 integrates outstanding electrical conductivity, air-caliber thermal insulation, high EMI shielding effectiveness of 75.0dB, and 90% strain compressibility with superb fatigue resistance. Intriguingly, thanks to the gel-like rheological behavior of the emulsion, ultralight graphene scaffolds with programmable geometries are obtained by 3D printing. This work provides a general approach for the preparation of ultralight and superelastic graphene aerogels with excellent EMI shielding properties, showing broad application prospects in various fields.

Combined physicochemical and transcriptomic analyses reveal the effect of the OsGA20ox1 gene on the starch properties of germinated brown rice

Genes play a pivotal role in regulating the germination of cereal grains; however, there is limited research on the impact of germination genes on the physicochemical properties of germinated cereal starch. We investigated the effects of the OsGA20ox1 gene on the multiscale structural features and adhesion behavior of germinated brown rice starch. Compared to the knockout lines group, the wild type exhibited a decrease in double-helix content (62.74 %), relative crystallinity (47.39 %), and short-range molecular ordering (2.47 %), accompanied by enhanced erosion on the surface of starch granules. The damage to glycosidic bonds at the double-helix level and the heightened structural amorphization (90.95 %) led to reduced entanglement and interaction among starch molecules, ultimately resulting in reduced characteristic viscosity. Further transcriptomic analysis revealed that OsGA20ox1 could regulate the expression of starch-related enzyme genes in the starch metabolism pathway during germination of brown rice. This study contributes to understanding the role of germination genes in promoting the physicochemical properties of starch in germinated grains, thereby opening up new avenues for the improvement of plant-based starch, and paving the way for further research in this field.

Hierarchical Assembly of 2D Covalent Organic Frameworks into Janus Optical Devices

AbstractAssembly of 2D molecules into hierarchical structures opens new avenues for creating multifunctional materials, yet fabricating the optical materials with multiscale structural features, and extending functions in a controllable way remains a challenge. Here, an asynchronous liquid depositing strategy is introduced that enables the asymmetrical growth of 2D covalent organic frameworks (COFs) on a graphene substrate. This results in Janus composite (JC) materials with independently controlled mesoscopic features, such as the COF layer thickness (100–300 nm), and its nanoflake length (0–200 nm), and thickness (0–30 nm). The JCs exhibit impressive light coupling capabilities and display a wide range of angle‐independent structural colors. These colors can be dynamically and reversibly changed in 10 ms when exposed to volatile organic compounds (VOCs). They also demonstrate remarkable tailorability to form both random and ordered patterns which shows excellent information processing potential. These unique properties make JCs highly suitable as novel responsive optical devices for collecting and processing information about their surroundings, as demonstrated by visual and real‐time VOC monitoring and localization in both open and confined spaces. It is believed that the controlled assembly of 2D components into composites with well‐organized hierarchical structures will facilitate the future development of extraordinary materials with extensive applications.

Improving the mechanical and thermal performance of bio-based concrete through multi-objective optimization

Bio-based concrete, which utilizes recycled agricultural waste as aggregates, has emerged as a sustainable alternative material in the construction industry. Optimizing its combined mechanical and thermal performance can promote its environmental and energy benefits. To achieve this, this paper presents a multi-objective optimization approach to improve the overall performance of bio-based concrete based on multiscale structural features, considering the porosity, shape, orientation, and volume fraction. Optimization strategies are provided for three main industrial applications. Furthermore, this study addresses multiple design requirements, including high-volume bio-aggregates, more demanding performance requirements, low densities, and humid environments. The optimization results demonstrate that the optimal solution varies with industrial applications and requirements. Consequently, we quantitatively provide specific optimal solutions for each case, and offer designers three preferences for mechanical properties, insulation, or balanced performance. Overall, this study optimizes the comprehensive performance of bio-based concrete in terms of multiscale structures, thereby contributing to a more sustainable construction industry.

Structure-Aware Cross-Modal Transformer for Depth Completion.

In this paper, we present a Structure-aware Cross-Modal Transformer (SCMT) to fully capture the 3D structures hidden in sparse depths for depth completion. Most existing methods learn to predict dense depths by taking depths as an additional channel of RGB images or learning 2D affinities to perform depth propagation. However, they fail to exploit 3D structures implied in the depth channel, thereby losing the informative 3D knowledge that provides important priors to distinguish the foreground and background features. Moreover, since these methods rely on the color textures of 2D images, it is challenging for them to handle poor-texture regions without the guidance of explicit 3D cues. To address this, we disentangle the hierarchical 3D scene-level structure from the RGB-D input and construct a pathway to make sharp depth boundaries and object shape outlines accessible to 2D features. Specifically, we extract 2D and 3D features from depth inputs and the back-projected point clouds respectively by building a two-stream network. To leverage 3D structures, we construct several cross-modal transformers to adaptively propagate multi-scale 3D structural features to the 2D stream, energizing 2D features with priors of object shapes and local geometries. Experimental results show that our SCMT achieves state-of-the-art performance on three popular outdoor (KITTI) and indoor (VOID and NYU) benchmarks.

Deep Learning Model for Quality Assessment of Urinary Bladder Ultrasound Images using Multi-scale and Higher-order Processing.

Autonomous Ultrasound Image Quality Assessment (US-IQA) is a promising tool to aid the interpretation by practicing sonographers and to enable the future robotization of ultrasound procedures. However, autonomous US-IQA has several challenges. Ultrasound images contain many spurious artifacts, such as noise due to handheld probe positioning, errors in the selection of probe parameters and patient respiration during the procedure. Further, these images are highly variable in appearance with respect to the individual patient's physiology. We propose to use a deep Convolutional Neural Network (CNN), USQNet, which utilizes a Multi-scale and Local-to-Global Second-order Pooling (MS-L2GSoP) classifier to conduct the sonographer-like assessment of image quality. This classifier first extracts features at multiple scales to encode the inter-patient anatomical variations, similar to a sonographer's understanding of anatomy. Then, it uses second-order pooling in the intermediate layers (local) and at the end of the network (global) to exploit the second-order statistical dependency of multi-scale structural and multi-region textural features. The L2GSoP will capture the higher-order relationships between different spatial locations and provide the seed for correlating local patches, much like a sonographer prioritizes regions across the image. We experimentally validated the USQNet for a new dataset of the human urinary bladder ultrasound images. The validation involved first with the subjective assessment by experienced radiologists' annotation, and then with state-of-the-art CNN networks for US-IQA and its ablated counterparts. The results demonstrate that USQNet achieves a remarkable accuracy of 92.4% and outperforms the SOTA models by 3 - 14% while requiring comparable computation time.

Polymer Bead Foams: A Review on Foam Preparation, Molding, and Interbead Bonding Mechanism

The diverse physical appearances and wide density range of polymer bead foams offer immense potential in various applications and future advancements. The multiscale and multilevel structural features of bead foams involve many fundamental scientific topics. This review presents a comprehensive overview of recent progress in the preparation and molding techniques of bead foams. Firstly, it gives a comparative analysis on the bead foam characteristics of distinct polymers. Then, a summary and comparison of molding techniques employed for fabricating bead foam parts are provided. Beyond traditional methods like steam-chest molding (SCM) and adhesive-assisted molding (AAM), emerging techniques like in-mold foaming and molding (IMFM) and microwave selective sintering (MSS) are highlighted. Lastly, the bonding mechanisms behind these diverse molding methods are discussed.

MsCNN-LSTM perimeter intrusion vibration signal identification method based on ultra-weak FBG arrays

To improve the recognition rate of ultra-weak fiber Bragg grating (UWFBG) arrays in perimeter security monitoring, this paper proposes a msCNN-LSTM combinatorial model recognition method, which combines an improved multi-scale convolutional neural network (msCNN) and a long short-term memory neural network (LSTM) to identify intrusion behaviors more accurately by synchronously extracting the multi-scale structural features and time-dependent relationships of vibration signals. Using msCNN to cross-learn multi-layer features, the hidden information between features at different scales can be obtained. The attention mechanism was applied to recalibrate the combined features extracted from the shallower layers, which enhanced the expression ability of features. Finally, the time dependence is analyzed by LSTM. Experimental results show that the scheme can effectively distinguish five typical intrusion behaviors, and the average recognition rate can reach 97.84%.

Unleashing multi-scale mechanical enhancement in NiTi shape memory alloy via annular intra-laser deposition with homogenized Ti2Ni nanoprecipitates

Additive manufacturing (AM) of composition-sensitive NiTi shape memory alloys (SMAs) is hindered by uncontrollable non-equilibrium microstructures, leading to functional performance degradation and limitations in high-throughput net-shaping applications. To address these challenges, we propose a novel approach combining annular intra-laser directed energy deposition (AIL-DED) with a tailored gradient post-treatment process. This enables the fabrication of nanocomposite microstructures in NiTi SMAs with precise control over the homogenization of Ti2Ni nanoprecipitates. In-situ monitoring and characterization of thermal history, microstrain, and constitutional phases, were utilized to uncover the relationship between multi-scale structural features and mechanical responses. The interplay between laser reflectivity, energy input, and lattice thermal vibrations affects forming quality in AIL-DED. Dynamic temperature variations were studied before reaching thermal equilibrium, revealing insights into thermal and mechanical interactions during the process. These findings contribute to understanding challenges in fabricating high-quality NiTi SMAs. Examining phase transformation behavior and its correlation with processing parameters deepens understanding of microstructural evolution and provides insights for tailoring material properties. The stress relaxation and dislocation evacuation at elevated temperatures caused the expansion of lattice and interplanar spacing in the age-treated SMA's B2 matrix phase, exhibiting a stable two-step phase transformation behavior (B19′↔R↔B2). AIL-DED combined with two-stage heat treatment strategy demonstrated enhanced homogeneity of non-equilibrium microstructures, resulting in the formation of high-density, well-dispersed Ti2Ni nanoprecipitates, with a semi-coherent interface to B2 matrix. The combination of load-bearing strengthening through well-dispersed Ti2Ni nanoprecipitates and Orowan strengthening at the semi-coherent interface yielded simultaneous enhancements in nanohardness, ultimate compressive strength, compressive fracture strain, and deformation recovery, following the AIL-DED combined with two-stage heat treatment process. The fracture mechanism transitioned from a mixture of brittle-ductile behavior to predominantly ductile characteristics. This study provides valuable insights that advance additive manufacturing and enhance the functional performance of SMAs in critical applications.

Micromechanical homogenization of a hydrogel-filled electrospun scaffold for tissue-engineered epicardial patching of the infarcted heart: a feasibility study

AbstractFor tissue engineering applications, accurate prediction of the effective mechanical properties of tissue scaffolds is critical. Open and closed cell modelling, mean-field homogenization theory, and finite element (FE) methods are theories and techniques currently used in conventional homogenization methods to estimate the equivalent mechanical properties of tissue-engineering scaffolds. This study aimed at developing a formulation to link the microscopic structure and macroscopic mechanics of a fibrous electrospun scaffold filled with a hydrogel for use as an epicardial patch for local support of the infarcted heart. The macroscopic elastic modulus of the scaffold was predicted to be 0.287 MPa with the FE method and 0.290 MPa with the closed-cell model for the realistic fibre structure of the scaffold, and 0.108 MPa and 0.540 MPa with mean-field homogenization for randomly oriented and completely aligned fibres. The homogenized constitutive description of the scaffold was implemented for an epicardial patch in a FE model of a human cardiac left ventricle to assess the effects of patching on myocardial mechanics and ventricular function in the presence of an infarct. Epicardial patching was predicted to reduce maximum myocardial stress in the infarcted LV from 19 kPa (no patch) to 9.5 kPa (patch) and to marginally improve the ventricular ejection fraction from 40% (no patch) to 43% (patch). This study demonstrates the feasibility of homogenization techniques to represent complex multiscale structural features in a simplified but meaningful and effective manner.

Recent Experimental Advances in Characterizing the Self-Assembly and Phase Behavior of Polypeptoids.

Polypeptoids are a family of synthetic peptidomimetic polymers featuring N-substituted polyglycine backbones with large chemical and structural diversity. Their synthetic accessibility, tunable property/functionality, and biological relevance make polypeptoids a promising platform for molecular biomimicry and various biotechnological applications. To gain insight into the relationship between the chemical structure, self-assembly behavior, and physicochemical properties of polypeptoids, many efforts have been made using thermal analysis, microscopy, scattering, and spectroscopic techniques. In this review, we summarize recent experimental investigations that have focused on the hierarchical self-assembly and phase behavior of polypeptoids in bulk, thin film, and solution states, highlighting the use of advanced characterization tools such as in situ microscopy and scattering techniques. These methods enable researchers to unravel multiscale structural features and assembly processes of polypeptoids over a wide range of length and time scales, thereby providing new insights into the structure-property relationship of these protein-mimetic materials.

Different multi-scale structural features of oat resistant starch prepared by ultrasound combined enzymatic hydrolysis affect its digestive properties

In this research, oat resistant starch (ORS) was prepared by autoclaving-retrogradation cycle (ORS-A), enzymatic hydrolysis (ORS-B), and ultrasound combined enzymatic hydrolysis (ORS-C). Differences in their structural features, physicochemical properties and digestive properties were studied. Results of particle size distribution, XRD, DSC, FTIR, SEM and in vitro digestion showed that ORS-C was a B + C-crystal, and ORS-C had a larger particle size, the smallest span value, the highest relative crystallinity, the most ordered and stable double helix structure, the roughest surface shape and strongest digestion resistance compared to ORS-A and ORS-B. Correlation analysis revealed that the digestion resistance of ORS-C was strongly positively correlated with RS content, amylose content, relative crystallinity and absorption peak intensity ratio of 1047/1022 cm−1 (R1047/1022), and weakly positively correlated with average particle size. These results provided theoretical support for the application of ORS-C with strong digestion resistance prepared by ultrasound combined enzymatic hydrolysis in the low GI food application.

Stacked graph bone region U-net with bone representation for hand pose estimation and semi-supervised training

3D hand estimation from 2D joint information is an essential task in human-machine interaction, which has achieved great progress as an application of deep learning. However, regression-based methods do not perform well because the structural information is not effectively exploited, and the joint coordinates are variable. To address these issues, the hand pose is represented with bone vectors instead of joint coordinates in this study, which are stabler to learn and allow for easier encoding of the hand geometric structure and joint dependency. A novel graph bone region U-Net is specifically designed for bone representation to learn multiscale structural features, where the proposed novel elements (graph convolution, pooling and unpooling) incorporate hand structural knowledge. Under the introduced “finger-to-hand” framework, the network gradually decreases the scale from bone to finger to hand for learning more meaningful multiscale features. Moreover, the unit network is stacked repeatedly to extract multilevel features. Based on the above network, a simple but effective semi-supervised approach is introduced to address the lack of 3D hand pose labels. Many experiments are conducted to evaluate the proposed approach on two challenging datasets. The experimental results show that the proposed supervised approach outperforms the state-of-the-art methods, and the proposed semi-supervised approach can still achieve favorable performance when the labeled data are scarce.

GRPAFusion: A Gradient Residual and Pyramid Attention-Based Multiscale Network for Multimodal Image Fusion

Multimodal image fusion aims to retain valid information from different modalities, remove redundant information to highlight critical targets, and maintain rich texture details in the fused image. However, current image fusion networks only use simple convolutional layers to extract features, ignoring global dependencies and channel contexts. This paper proposes GRPAFusion, a multimodal image fusion framework based on gradient residual and pyramid attention. The framework uses multiscale gradient residual blocks to extract multiscale structural features and multigranularity detail features from the source image. The depth features from different modalities were adaptively corrected for inter-channel responses using a pyramid split attention module to generate high-quality fused images. Experimental results on public datasets indicated that GRPAFusion outperforms the current fusion methods in subjective and objective evaluations.

Remote Sensing Scene Image Classification Based on mmsCNN–HMM with Stacking Ensemble Model

The development of convolution neural networks (CNNs) has become a significant means to solve the problem of remote sensing scene image classification. However, well-performing CNNs generally have high complexity and are prone to overfitting. To handle the above problem, we present a new classification approach using an mmsCNN–HMM combined model with stacking ensemble mechanism in this paper. First of all, a modified multi-scale convolution neural network (mmsCNN) is proposed to extract multi-scale structural features, which has a lightweight structure and can avoid high computational complexity. Then, we utilize a hidden Markov model (HMM) to mine the context information of the extracted features of the whole sample image. For different categories of scene images, the corresponding HMM is trained and all the trained HMMs form an HMM group. In addition, our approach is based on a stacking ensemble learning scheme, in which the preliminary predicted values generated by the HMM group are used in an extreme gradient boosting (XGBoost) model to generate the final prediction. This stacking ensemble learning mechanism integrates multiple models to make decisions together, which can effectively prevent overfitting while ensuring accuracy. Finally, the trained XGBoost model conducts the scene category prediction. In this paper, the six most widely used remote sensing scene datasets, UCM, RSSCN, SIRI-WHU, WHU-RS, AID, and NWPU, are selected to carry out all kinds of experiments. The numerical experiments verify that the proposed approach shows more important advantages than the advanced approaches.

Pea-Resistant Starch with Different Multi-scale Structural Features Attenuates the Obesity-Related Physiological Changes in High-Fat Diet Mice.

The present study compared the modulatory effects of different resistant starches (RSs) isolated from native (NP-RS), acid-hydrolyzed (AHP-RS), and pullulanase debranched (PDP-RS) pea starches on the corresponding in vivo metabolic responses in high fat (HF)-diet-induced obese mice. The biochemical studies on serum lipid profile and antioxidant enzyme activities were supported by histological and gene expression analyses, which suggested a potential therapeutic role for RS in regulating obesity, possibly through the production of short-chain fatty acids and the proliferation of some beneficial colonic bacteria, including Allobaculum, Bifidobacterium, Odoribacter, Clostridium, and Prevotella. Particularly, a more pronounced effect of AHP-RS with a higher proportion of the crystalline region and a more ordered double-helical alignment on improving the hyperlipidemic symptoms in obese mice induced by a HF diet was observed. Our analysis revealed that the RS3 samples seemed to be more effective than RS2 in terms of attenuating obesity in mice that were fed a HF diet.

Freeze-derived heterogeneous structural color films

Structural colors have a demonstrated value in constructing various functional materials. Efforts in this area are devoted to developing stratagem for generating heterogeneous structurally colored materials with new architectures and functions. Here, inspired by icing process in nature and ice-templating technologies, we present freeze-derived heterogeneous structural color hydrogels with multiscale structural and functional features. We find that the space-occupying effect of ice crystals is helpful for tuning the distance of non-close-packed colloidal crystal nanoparticles, resulting in corresponding reflection wavelength shifts in the icing area. Thus, by effectively controlling the growth of ice crystals and photo-polymerizing them, structural color hydrogels with the desired structures and morphologies can be customized. Other than traditional monochromatic structure color hydrogels, the resultant hydrogels can be imparted with heterogeneous structured multi-compartment body and multi-color with designed patterns through varying the freezing area design. Based on these features, we have also explored the potential value of these heterotypic structural color hydrogels for information encryptions and decryptions by creating spatiotemporally controlled icing areas. We believe that these inverse ice-template structural color hydrogels will offer new routes for the construction and modulation of next generation smart materials with desired complex architectures.