Programmable Design Research Articles

Programmable Design and Fabrication of 3D Variable–Stiffness Structure Based on Patterned Graphene‐Heating Network

The 3D variable–stiffness structure can realize shape programming, reconstruction, adaptation, and locking, and therefore, it has a wide design creation space. Accurate local stiffness control is of considerable significance to the design and application of 3D variable–stiffness structures although it is challenging. Herein, a 3D variable–stiffness structure realization scheme based on a patterned heating network is introduced. The laser‐engraving and 3D‐printing technologies are combined to obtain a 3D variable–stiffness structure composed of a patterned graphene‐heating network (PGHN) and polylactic acid (PLA). The proposed scheme uses PGHN to accurately control the local stiffness of 3D PLA and realize programmable design and fabrication of 3D variable–stiffness structures. The “torsional structure,” “hexagonal structure,” and “spring” cases are used to elaborate the designability, excellent deformation and reconstruction capacity, and reasonable load bearing capacity of the PGHN/PLA variable–stiffness structure. A pneumatic disc, which is used as a reference for studies on shape control of PGHN/PLA variable–stiffness structures, is designed. Also, a pneumatic robot is designed based on the local stiffness control and shape‐locking function of PGHN/PLA to achieve multimode motion control using a single air source. The PGHN/PLA variable–stiffness structure has potential applications in multimode robots, wearable devices, and deployable structures.

High-throughput, modular and logic analysis platform based on binary photonic crystals array and entropy-driven amplifier for multiplex biomarkers detection

The development of high-throughput and modular assay of multiplex biomarkers has great significance for disease diagnosis but it still faces some difficulty. Herein, a high-throughput, modular and logic analysis platform combining binary photonic crystals (PCs) array and entropy-driven DNA amplifier (EDA) was developed to achieve simple, high-sensitive, and nonenzymic detection of multiplex biomarkers. In this platform, three modules including logic-recognition, EDA and binary PCs array were integrated, and each module had independent and universal design but connected functions. The binary PCs array was fabricated conveniently through stacking self-assembly of two kinds of colloidal nanospheres, and could specifically enhance two kinds of fluorescence based on the match between emission peak and photonic band gap. Functionally, the input of two mRNA biomarkers initiated the logical operation of logic-recognition module, and the released DNA strand triggered corresponding EDA module for amplified fluorescence signal output (FAM or Cy5). The whole DNA circuit was performed in one step. Then, binary PCs array module further enhanced both two fluorescence signals, and got direct fluorescence signal readout by microplate reader (scanning rate of 96 samples/ 7 min). It is the first time that the binary PCs array-based high-throughput platform has achieved logical and sensitive detection of multi-biomarkers by employing logic gate and EDA. Therefore, the proposed platform has merits of logical analysis, high sensitivity, programmable design, high throughput and multiplex detection, showing potential application in bioanalysis and clinical diagnostics.

Approaching Elaborate Control of the Nano-Products of Carbothermal Reduction Reaction Through In Situ Identification.

Atomic understanding of a chemical reaction can realize the programmable design and synthesis of desired products with specific compositions and structures. Through directly monitoring the phase transition and tracking the dynamic evolution of atoms in a chemical reaction, in situ transmission electron microscopy (TEM) techniques offer the feasibility of revealing the reaction kinetics at the atomic level. Nevertheless, such investigation is quite challenging, especially for reactions involving multi-phase and complex interfaces, such as the widely adopted carbothermal reduction (CTR) reactions. Herein, in-situ TEM is applied to monitor the CTR of Co3 O4 nanocubes on reduced graphene oxide nanosheets. Together with the first-principle calculation, the migration route of Co atoms during the phase transition of the CTR reaction is revealed. Meanwhile, the interfacial edge-dislocations/stress-gradient is identified as a result of the atomistic diffusion, which in turn can affect the morphology variation of the reactants. Accordingly, controllable synthesis of Co-based nanostructure with a desirable phase and structure has been achieved. This work not only provides atomic kinetic insight into CTR reactions but also offers a novel strategy for the design and synthesis of functional nanostructures for emerging energy technologies.

Tailored Functionalized Protein Nanocarriers for Cancer Therapy: Recent Developments and Prospects.

Recently, the potential use of nanoparticles for the targeted delivery of therapeutic and diagnostic agents has garnered increased interest. Several nanoparticle drug delivery systems have been developed for cancer treatment. Typically, protein-based nanocarriers offer several advantages, including biodegradability and biocompatibility. Using genetic engineering or chemical conjugation approaches, well-known naturally occurring protein nanoparticles can be further prepared, engineered, and functionalized in their self-assembly to meet the demands of clinical production efficiency. Accordingly, promising protein nanoparticles have been developed with outstanding tumor-targeting capabilities, ultimately overcoming multidrug resistance issues, in vivo delivery barriers, and mimicking the tumor microenvironment. Bioinspired by natural nanoparticles, advanced computational techniques have been harnessed for the programmable design of highly homogenous protein nanoparticles, which could open new routes for the rational design of vaccines and drug formulations. The current review aims to present several significant advancements made in protein nanoparticle technology, and their use in cancer therapy. Additionally, tailored construction methods and therapeutic applications of engineered protein-based nanoparticles are discussed.

Mechanical programmable design of soft pneumatic actuators with kirigami patterns to generate expanding, bending and twisting motions

<abstract><p>This paper presents the influence of cuts inspired by kirigami art on the outer surface of a soft actuator with cylindrical geometry. Both the kirigami pattern and orientation were varied to achieve expanding, bending, and twisting motions. Two symmetric kirigami patterns are presented, i.e., cells and stripes, in a horizontal, vertical, and tilted configurations, respectively. We also present the tensile response of the kirigami sheets by using the finite element method (FEM). Then, we show that the soft actuator expands, bends or twists when kirigami patterns are wrapped around it. All actuators were analyzed by performing FEM simulations to evaluate the effects of kirigami patterns on the actuator performance. Among the obtained variants of the expanding and bending actuators, the striped pattern provided the best performance for both motions. We show how the number of cuts and the relationship between the gaps affect the expanding performance. For the twisting motion, we obtained two variants; we present the influence of the change of the inclination angle for both patterns, i.e., cells and stripes. The actuators that presented the best performance for each motion were built; a manufacturing process using the casting technique is described. The validation of the hardware by computer vision is presented.</p></abstract>

A coarse-grained simulation model for colloidal self-assembly via explicit mobile binders.

Colloidal particles with mobile binding molecules constitute a powerful platform for probing the physics of self-assembly. Binding molecules are free to diffuse and rearrange on the surface, giving rise to spontaneous control over the number of droplet-droplet bonds, i.e., valence, as a function of the concentration of binders. This type of valence control has been realized experimentally by tuning the interaction strength between DNA-coated emulsion droplets. Optimizing for valence two yields droplet polymer chains, termed 'colloidomers', which have recently been used to probe the physics of folding. To understand the underlying self-assembly mechanisms, here we present a coarse-grained molecular dynamics (CGMD) model to study the self-assembly of this class of systems using explicit representations of mobile binding sites. We explore how valence of assembled structures can be tuned through kinetic control in the strong binding limit. More specifically, we optimize experimental control parameters to obtain the highest yield of long linear colloidomer chains. Subsequently tuning the dynamics of binding and unbinding via a temperature-dependent model allows us to observe a heptamer chain collapse into all possible rigid structures, in good agreement with recent folding experiments. Our CGMD platform and dynamic bonding model (implemented as an open-source custom plugin to HOOMD-Blue) reveal the molecular features governing the binding patch size and valence control, and opens the study of pathways in colloidomer folding. This model can therefore guide programmable design in experiments.

Framework nucleic acids directed assembly of Au nanostructures for biomedical applications

AbstractAu nanostructures have elicited considerable interest because of their unique optical and physicochemical properties. These characteristics are mostly depended on the size, shape, chemical composition and dielectric environment of Au nanoparticles (AuNPs). Because of the easy chemical modification and programmable design of DNA molecules, they can not only combine to the surface of AuNP for improved stability, but also form the framework nucleic acids (FNAs) for direct assembly of different Au nanostructures with precise configuration and multiple dimensions. This paper starts with the synthesis and properties of Au nanomaterials. Followed by a brief introduction to the development of FNA nanotechnology, the FNA‐directed assembly of Au nanostructures is discussed. Then, we focus on the biomedical applications of these Au nanostructures, including biosensing and therapeutics. In the end, we envision an insight into the remaining challenges and future perspectives toward FNA‐based metal functional materials.

The Programmable Design of Large-Area Piezoresistive Textile Sensors Using Manufacturing by Jacquard Processing.

Among wearable e-textiles, conductive textile yarns are of particular interest because they can be used as flexible and wearable sensors without affecting the usual properties and comfort of the textiles. Firstly, this study proposed three types of piezoresistive textile sensors, namely, single-layer, double-layer, and quadruple-layer, to be made by the Jacquard processing method. This method enables the programmable design of the sensor’s structure and customizes the sensor’s sensitivity to work more efficiently in personalized applications. Secondly, the sensor range and coefficient of determination showed that the sensor is reliable and suitable for many applications. The dimensions of the proposed sensors are 20 × 20 cm, and the thicknesses are under 0.52 mm. The entire area of the sensor is a pressure-sensitive spot. Thirdly, the effect of layer density on the performance of the sensors showed that the single-layer pressure sensor has a thinner thickness and faster response time than the multilayer pressure sensor. Moreover, the sensors have a quick response time (<50 ms) and small hysteresis. Finally, the hysteresis will increase according to the number of conductive layers. Many tests were carried out, which can provide an excellent knowledge database in the context of large-area piezoresistive textile sensors using manufacturing by Jacquard processing. The effects of the percolation of CNTs, thickness, and sheet resistance on the performance of sensors were investigated. The structural and surface morphology of coating samples and SWCNTs were evaluated by using a scanning electron microscope. The structure of the proposed sensor is expected to be an essential step toward realizing wearable signal sensing for next-generation personalized applications.

Solvent triggered shape morphism of 4D printed hydrogels

4D printing of smart materials is a viable field of research for fabricating dynamic structures for various biomedical applications. 4D printing of hydrogel structures is challenging due to poor printability of hydrogels and poor shape fidelity of printed patterns when direct-ink writing-based 3D printers are used. In this study, chitosan (CS) hydrogel ink cross-linked with citric acid (CA) was made printable, exhibiting a shape-morphing behaviour when exposed to solvent as an external trigger. As one side of a printed structure is exposed to solvent, the solvent diffuses in and a concentration gradient is developed across the section. This concentration gradient results in displacement field, which finally leads to out-of-plane bending of the structure. This actuation is irreversible in nature since the concentration gradient is maintained between hydrophilic chitosan and hydrophobic silane layers. The reversibility of the morphed structures was achieved by dipping them in ethanol, which takes up solvent over time and thus diminishes the concentration gradient.The optimized CS/CA ink exhibited excellent rheological properties, with good extrudability and shape fidelity of printed structures. The chitosan ink was printed into various complex 3D architectures and was modified by hydrophobic coating of trimethyl silane (TMS). These hydrophobic patterns were coated on printed structures with varied interspacing, angles, and hinges to generate programmable designs. A printed soft gripper was demonstrated as an application that could lift an object seven times its weight. The shape morphing CS/CA hydrogel with excellent printability exhibits potential for 4D printing that has wide applications in soft robots, actuators, grippers, and sensors.

Femtosecond laser hybrid processing strategy of transparent hard and brittle materials.

With high hardness, high thermal stability, chemical inertness and excellent optoelectronic properties, transparent hard and brittle materials have drawn significant attentions in frontier domains such as aerospace, photoelectric detection, and high-intensity lasers. Femtosecond laser processing technology demonstrates great potential for transparent hard and brittle materials processing due to its outstanding advantages such as non-contact, true 3D processing and programmable design. However, high-energy laser ablation usually causes severe damage to the surface of the materials, resulting in low processing accuracy, low processing efficiency and poor surface quality. Femtosecond laser hybrid processing strategies have been proven to be an effective solution to solve the above problems. This mini-review summarizes the fundamentals and research progress of femtosecond laser hybrid processing strategies of transparent hard and brittle materials in recent years. Moreover, the challenges and application prospects of these techniques are discussed.

A Programmable Plasmonic Gas Microsystem for Detecting Arbitrarily Combinated Volatile Organic Compounds (VOCs) with Ultrahigh Resolution.

For gas sensors, the ultrasensitive and highly selective detection of multiple components is of great significance in a wide range of applications extending from environment to healthcare, which is still a long-term challenge due to the single sensing mechanism of most sensors. Here, we combine the advantages of microfluidic chips and surface-enhanced Raman spectroscopy (SERS) spectra to fabricate a smart single-chip for simultaneously detecting an arbitrary combination of VOCs that incorporates different detection units, working on either a physisorption or chemisorption mechanism. Full integration of microfluidic and multiplex nanostructure components on one chip permits programmable design for sensing multifarious volatile compounds, and enables on-chip signal amplifications with increased reproducibility. As a proof-of-principle experiment, we demonstrate the simultaneous identification of 9 different gases that belong to aromatic compounds, aldehydes, ketones, or sulfides in one mixture, with high sensitivity (ppb level), high selectivity, and high robustness (error ∼8%). We further evaluated the application of our universal gas sensor in two scenarios including indoor air pollution monitoring and exhaled breath-based disease diagnosis. We expect that our design will improve the various practical applications of gas sensors.

Mesh/membrane composite with superior mechanical performance: A deep learning-based design

The mesh/membrane composite structure in aerospace and engineering applications is often susceptible to degradation or failure due to lack of stiffness and strength. Here, we propose a method to design the mesh/membrane composite based on deep learning technique, trained with a database of thousands of structures from finite element simulations. Our method provides a programmable design solution to screen out desired candidates of mesh with superior fracture strength in specified relative density and porosity. Results show that the method can offer designs with different failure modes and crack regions for various target functionalities, while maintaining light weight and superior fracture strength. Building on the paradigm of smart design, we suggest potential strategies for designing the mesh/membrane composite with desired mechanical performance.

PH Programmed Optical Sensor Arrays for Cancer Plasma Straightforward Discrimination Based on Protein-Responsive Patterns.

Optical cross-reactive sensor arrays inspired by the mammalian olfactory system that can realize straightforward discrimination of plasma from cancer patients hold great potential for point-of-care diseases diagnostics. Herein, a pH programmed fluorescence sensor array based on protein-responsive patterns was designed for straightforward discrimination of different types of cancer plasma. It is worth noting that plasma discrimination can be realized only by programming one nanomaterial using different pH values, which greatly simplifies the programmable design of the sensor array, making it an important highlight of this work. In addition, the mechanism of the pH programmed fluorescence sensor array for protein responsiveness was systematically investigated through molecular docking simulation, fluorescence resonance energy transfer (FRET), and fluorescence lifetime experiments. Most importantly, not only can the differences between plasma from healthy people and and from patients with different cancer species including gastric cancer, liver cancer, breast cancer, and cervical cancer be discriminated by this pH programmed fluorescence sensor array, but also the blind test of unknown plasma samples can be well identified with 100% accuracy, indicating its promising prospect in clinical application.

Programmable design and self assembly of peptide conjugated AIEgens for biomedical applications

Aggregation-induced emission luminogens (AIEgens) possess enhanced fluorescence in highly aggregated states, thus enabling AIEgens as a promising module for highly emissive fluorescence biomaterials. So far, AIEgens-based nanomaterials and their hybrids have been reported for biomedical applications. Benefiting from the intrinsic biocompatibility and biofunction-editing properties of peptides, peptide-AIEgens hybrid biomaterials reveal unlimited possibilities including target capacity, specificity, stimuli-responsiveness, self-assembly, controllable structural transformation, etc.. In the last two decades, peptide-AIEgens hybrid nanomaterials with a unique design concept in aggregated states have achieved various biomedical applications such as biosensing, bioimaging, imaging-guided surgery, drug delivery and therapy. More recently, programmable design of peptide-AIEgens for in situ self-assembly provides a unique strategy for constructing intelligent entities with defined biological functions. In this review, we summarize the basic design principle of programmable peptide-AIEgens, structure-effect relationship and their unusual biomedical effects. Finally, an outlook and perspective toward future challenges and developments of peptide-AIEgens nanomaterials are concluded.

3D chiral mechanical metamaterial for tailored band gap and manipulation of vibration isolation

This paper studies the dynamic properties of a 3D chiral mechanical metamaterial, which largely depend on tacticity describing the connection forms of the chiral unit. Based on the finite element theory and Bloch's theorem, a dynamic model of the 3D chiral mechanical metamaterial is established to analyze the band structures and transmission responses. The isotactic configuration converts longitudinal waves into transverse waves due to the chirality, which results in the vibration attenuation of the longitudinal wave that is independent on band gaps. The band gaps appear at the band structures of the syndiotactic configuration, and the generation mechanism is related to the vibration mode of ligaments, which can be used to predict band gap. The samples were manufactured by additive manufacturing to vibration transmission test for verifying the dynamic responses and the band gaps of the 3D chiral mechanical metamaterial with different tacticities. In addition, the gradient design and programmable design are used to further expand the vibration attenuation region, which has the potential for practical applications of vibration suppression.

CAR-T Therapy in Clinical Practice: Technical Advances and Current Challenges.

Chimeric antigen receptors (CAR) redirect T cells to specifically recognize and eliminate tumor cells. CAR-T therapy has achieved successful clinical outcomes, and it has been transformed into commercially available products to treat acute lymphoblastic leukemia and B cell lymphoma. These breakthroughs have motivated hundreds of CAR-T clinical trials initiated each year, with ≈900 cases registered on the ClinicalTrials website till 2021. Accumulating clinical experiences have highlighted some limitations of this strategy, e.g., relapse after complete response, poor efficacy in solid tumors, on-target off-tumor toxicities, lack of persistence, and tumor resistance. These challenges limit the therapeutic application of CAR-T cells. Multidisciplinary approaches are actively investigated to address these issues. In this review, the antigens, CAR designs, and cell sources are summarized in clinical trials from 2020 to 2021. The innovative modular and programmable designs in CAR-T cells, including advances in signaling domains, antigen-recognition domains, T cell engineering, and cell resources, are further discussed. Integrative genetic and chemical engineering strategies are promising to improve the versatility, antitumor efficacy, persistence, and safety of CAR-T cells. In the future, the next generation of CAR-T cell therapies will offer more options for patients who are refractory to standard tumor therapies.

Programmable design of isothermal nucleic acid diagnostic assays through abstraction-based models

Accelerating the design of nucleic acid amplification methods remains a critical challenge in the development of molecular tools to identify biomarkers to diagnose both infectious and non-communicable diseases. Many of the principles that underpin these mechanisms are often complex and can require iterative optimisation. Here we focus on creating a generalisable isothermal nucleic acid amplification methodology, describing the systematic implementation of abstraction-based models for the algorithmic design and application of assays. We demonstrate the simplicity, ease and flexibility of our approach using a software tool that provides amplification schemes de novo, based upon a user-input target sequence. The abstraction of reaction network predicts multiple reaction pathways across different strategies, facilitating assay optimisation for specific applications, including the ready design of multiplexed tests for short nucleic acid sequence miRNAs or for difficult pathogenic targets, such as highly mutating viruses.

Amino-Acid-Encoded Supramolecular Photothermal Nanomedicine for Enhanced Cancer Therapy.

Photothermal nanomedicine based on self-assembly of biological components, with excellent biosafety and customized performance, is vital significance for precision cancer therapy. However, the programmable design of photothermal nanomedicine remains extremely challenging due to the vulnerability and variability of noncovalent interactions governing supramolecular self-assembly. Herein, it is reported that amino acid encoding is a facile and potent means to design and construct supramolecular photothermal nanodrugs with controlled therapeutic activities. It is found that the amount and type of amino acid dominates the assembled nanostructures, structural stability, energy-conversion pathway, and therapeutic mechanism of the resulting nanodrugs. Two optimized nanodrugs are endowed with robust structural integrity against disassembly along with high photothermal conversion efficiency, efficient cellular internalization, and enhanced tumor accumulation, which result in more efficient tumor ablation. This work demonstrates that design based on amino acid encoding offers an unprecedented opportunity for the construction of remarkable photoactive nanomedicines toward cancer diagnostics and therapeutics.

Recent Advances in Stimuli-Responsive DNA-Based Hydrogels.

Stimuli-responsive DNA-based hydrogels are attracting growing interest because of their smart responsiveness, excellent biocompatibility, regulated biodegradability, and programmable design properties. Integration of reconfigurable DNA architectures and switchable supramolecular moieties (as cross-linkers) in hydrogels by responding to external stimuli provides an ideal approach for the reversible tuning structural and mechanical properties of the hydrogels, which can be exploited in the development of intelligent DNA-based materials. This review highlights recent advances in the design of responsive pure DNA hydrogels, DNA-polymer hybrid hydrogels, and autonomous DNA-based hydrogels with transient behaviors. A variety of chemically and physically triggered DNA-based stimuli-responsive hydrogels and their versatile applications in biosensing, biocatalysis, cell culture and separation, drug delivery, shape memory, self-healing, and robotic actuators are summarized. Finally, we address the key challenges that the field will face in the coming years, and future prospects are identified.

Soft Actuator with Programmable Design: Modeling, Prototyping, and Applications.

Designs of soft actuators are mostly guided and limited to certain target functionalities. This article presents a novel programmable design for soft pneumatic bellows-shaped actuators with distinct motions, thus a wide range of functionalities can be engendered through tuning channel parameters. According to the design principle, a kinematic model is established for motion prediction, and a sampling-based optimal parameter search is executed for automatic design. The proposed design method and kinematic models provide a tool for the generation of an optimal channel curve, with respect to target functions and required motion trajectories. Quantitative characterizations on the analytical model are conducted. To validate the functionalities, we generate three types of actuators to cover a wide range of motions in manipulation and locomotion tasks. Comparisons of model prediction on motion trajectory and prototype performance indicate the efficacy of the forward kinematics, and two task-based optimal designs for manipulation scenarios validate the effectiveness of the design parameter search. Prototyped by additive manufacturing technique with soft matter, multifunctional robots in case studies have been demonstrated, suggesting adaptability of the structure and convenience of the soft actuator's automatic design in both manipulation and locomotion. Results show that the novel design method together with the kinematic model paves a way for designing function-oriented actuators in an automatic flow.