Two-dimensional metal-organic frameworks (2D-MOFs) have been extensively studied as promising materials in the fields of eletrocatalysis, drug delivery, electronic devicese, etc. However, few studies have explored the application potential of 2D-MOFs in novel neuromorphic computing devices. In this work, we report an optoelectronic neuromorphic transistor based on a 2D-MOFs/polymer charge-trapping layer. We found that, the large specific surface area, stable crystal structure, and highly accessible active sites in 2D-MOFs make them excellent charge-trapping materials for our devices, which are beneficial for mimicking the memory and learning functions observed in the organism's nervous systems. Different types of synaptic behaviors have been realized in our 2D-MOFs-based neuromorphic devices under stimuli signal, e.g., paired-pulse facilitation, excitatory post-synaptic current, short-term memory, and long-term memory. More interestingly, emotion-adjustable learning behavior was realized by changing the value of the source-drain voltage. This work can shed light on the application of 2D-MOFs in neuromorphic computing and will contribute to the further development of neuromorphic computing devices. Corresponding authors Email: [email protected] (Jia Huang) [email protected] (Shilei Dai)
This Supporting Information includes: a comparison of the REAL (Robot Ear Accomplished by Laser) with a typical vibration measuring system (Laser Doppler Vibrometers, LDV), frequency response of various materials on REAL and real-time analysis of REAL audio neural network model. Xiaoping Hong Email: [email protected]
"AI & Drug Discovery" mode has significantly promoted drug development and achieved excellent performance, especially with the rapid development of deep learning, making remarkable contributions to protecting human physiological health. However, due to the "black-box" characteristic of the deep learning model, the decision route and predicted results in different research stages assisted by deep models are usually unexplainable, limiting their application in practice and more in-depth research of drug discovery. Focusing on the drug molecules, we propose an explainable fragment-based molecular property attribution technique for analyzing the influence of particular molecule fragments on properties and the relationship between the molecular properties in this paper. Quantitative experiments on 42 benchmark property tasks demonstrate that 325 attribution fragments, which account for 90% of the overall attribution results obtained by the proposed method, have positive relevance to the corresponding property tasks. More impressively, most of the attribution results randomly selected are consistent with the existing mechanism explanations. The discovery mentioned above provides a reference standard for assisting researchers in developing more specific and practical drug molecule studies, such as synthesizing molecular with the targeted property using a fragment obtained from the attribution method.Corresponding author(s) Email: [email protected]
The integration of an ingestible dosage form with sensing, actuation and drug delivery capabilities can enable a broad range of surgical-free diagnostic and treatment strategies. However, the gastrointestinal (GI) tract is a highly constrained and complex luminal construct that fundamentally limits the size of an ingestible system. Recent advancements in mesoscale magnetic crawlers have demonstrated the ability to effectively traverse complex and confined systems by leveraging magnetic fields to induce contraction and bending-based locomotion. However, the integration of functional components (e.g., electronics) in the proposed ingestible system remains fundamentally challenging. Here, we demonstrate the creation of a centralized compartment in a magnetic robot by imparting localized flexibility (MR-LF). The centralized compartment enables MR-LF to be readily integrated with modular functional components and payloads, such as commercial off-the-shelf electronics and medication, while preserving its bidirectionality in an ingestible form factor. We demonstrate the ability of MR-LF to incorporate electronics, perform drug delivery, guide continuum devices such as catheters, and navigate air-water environments in confined lumens. The MR-LF enables functional integration to create a highly-integrated ingestible system that can ultimately address a broad range of unmet clinical needs. Keywords: Ingestible electronics; ingestible robots; soft robots; magnetic robots; magnetic crawlers; drug delivery. Corresponding author email: [email protected]
The substantial increase in global population and climate change, among other factors have led to global food security and supply chain challenges. The United Nations has laid out an agenda to sustainably achieve zero hunger by 2030 as one of its sustainable development goals. However, sustainably achieving improved food yield has become a challenge as excessive use of fertilizers has also led to adverse environmental impact. To address the aforementioned challenges, WisDM Green, an artificial intelligence (AI)-based platform that aims to pinpoint and prioritize compound (e.g. biostimulants) combinations in peat moss, is harnessed to sustainably improve the yield of Amaranthus cruentus (red spinach). In this proof-of-concept study, from a pool of 8 compounds, WisDM Green-pinpointed combinations (6-Benzylaminopurine/Ethylenediaminetetraacetic Acid Iron (III) and Humic Acid/Seaweed Extract) achieve 26.34±15.80 and 33.59±14.60 increase in %Yield, respectively. The study also indicates that compound combinations may exhibit concentration-dependent synergies and thus, properly adjusting the concentration ratios of combinations may further improve plant yield in the context of sustainable farming. P. Wang and K. You contributed to this work equally.Corresponding author(s) Email: [email protected], [email protected], [email protected]
Deformability and self-adaptability are important for soft robots in order to deal with uncertain and varying situations and environments during movement and navigation. Droplet-based robots are great candidates to travel inside narrow and constrained spaces without damaging the interfaces due to their extreme deformability and liquid nature, which enables smooth contact between robots and target spaces. Here, we propose magnetic liquid metal droplet robots, comprising liquid metal and carbonyl iron, that can perform reversible telescopic deformation, bending, and on-demand locomotion. The magnetic liquid metal-based robots can perform on demand and reversible coalescence and splitting by intricately applying magnetic fields. Importantly, the liquid metal robot can perform phase transition to fix the desired shape after the programmable shape encoding. The liquid metal-based soft robots can serve as dynamic and recyclable switches for complex circuits, and are capable of repairing damaged sections of microcircuits by remote actuation, controllable coalescence, and on-demand circuit welding. The technology provides a new application scenario of droplet-based soft robots for on-demand circuit welding and transient recyclable electronics. Corresponding author(s) Email: X.Z.: [email protected]; L.Z.: [email protected]; B.W.: [email protected]
Complex environments, such as those found in surgical and search-and-rescue applications, require soft devices to adapt to minimal space conditions without sacrificing the ability to complete dexterous tasks. Stacked Balloon Actuators (SBAs) are capable of large deformations despite folding nearly flat when deflated, making them ideal candidates for such applications. This paper presents the design, fabrication, modeling, and characterization of monolithic, inflatable, soft SBAs. Modeling is presented using analytical principles based on geometry, and then using conventional and real-time finite element methods. Both one and three degree-of-freedom (DoF) SBAs are fully characterized with regards to stroke, force, and workspace. Finally, three representative demonstrations show the SBA's small-aperture navigation, bracing, and workspace-enhancing capabilities.
Human receives and transmits various information from the outside world through different sensory systems. The sensory neurons integrate various sensory inputs into a synthetical perception to monitor complex environments, and this fundamentally determines the way how we perceive the world. Developing multifunctional artificial sensory elements that can integrate multisensory perception plays a vital role in future intelligent perception systems, whereas prior spiking neurons reported can only handle single-mode physical signals. Here, we present a bio-inspired haptic-temperature fusion spiking neuron based upon a serial connection of piezoresistive sensor and VO2 volatile memristor. The artificial sensory neuron is capable of detecting and encoding pressure and temperature inputs based on the voltage dividing effect and the intrinsic thermal sensitivity of metal-insulator transition in VO2. Recognition of Braille characters is achieved through multiple piezoresistive sensors, taking advantage of the spatial integration capabilities of such spiking neurons. Notably, the traditionally separate haptic and temperature signals can be fused physically in the sensory neuron when synchronizing the two sensory cues, which is able to recognize multimodal haptic/temperature patterns. The artificial multisensory neuron thus provides a promising approach towards e-skin, neuro-robotics and human-machine interaction technologies.
Developing on-site biomarker enrichment platforms could help to improve the diagnosis of gastrointestinal (GI) tract diseases at early stages. Medical procedures, such as colonoscopies and imaging techniques, are used to diagnose disease, but are not easily accessible for repeat measurements. In the other hand, liquid biopsies, e.g., blood, urine, or fecal samples, have become important sampling strategies to identify health concerns. Herein, a robotic pill is designed for collecting relevant biomarkers from the GI over prolonged sampling periods. The robotic pill comprises a magnetic core for locomotion, a delayed gate mechanism that controls sampling location based on changes in its environment, and an enrichment module that traps biomarkers in an absorbent matrix while enabling biofluid to pass through the chamber. The robotic pill was assessed to sample microparticles, proteins, and bacteria from solution. Moreover, the robotic pill was capable of directed locomotion in complex environments and docking in a targeted region against fluid flow. Utilization of an untethered robotic sampling system could provide a tool to investigate aspects of disease initiation and progression for early diagnosis and therapy monitoring.
Avi Hazan1, Elishai Ezra Tsur1*1 Neuro-Biomorphic Engineering Lab, Department of Mathematics and Computer Science, The Open University of Israel, Ra’anana, Israel* Correspondence: [email protected] hardware designs realize neural principles in electronics to provide high-performing, energy-efficient frameworks for machine learning. Here, we propose a neuromorphic analog design for continuous real-time learning. Our hardware design realizes the underlying principles of the neural engineering framework (NEF). NEF brings forth a theoretical framework for the representation and transformation of mathematical constructs with spiking neurons. Thus, providing efficient means for neuromorphic machine learning and the design of intricate dynamical systems. Our analog circuit design implements the neuromorphic prescribed error sensitivity (PES) learning rule with OZ neurons. OZ is an analog implementation of a spiking neuron, which was shown to have complete correspondence with NEF across firing rates, encoding vectors, and intercepts. We demonstrate PES-based neuromorphic representation of mathematical constructs with varying neuron configurations, the transformation of mathematical constructs, and the construction of a dynamical system with the design of an inducible leaky oscillator. We further designed a circuit emulator, allowing the evaluation of our electrical designs on a large scale. We used the circuit emulator in conjunction with a robot simulator to demonstrate adaptive learning-based control of a robotic arm with six degrees of freedom.
A prominent problem in computer vision is occlusion, which occurs when an object’s key features temporarily disappear behind another crossing body, causing the computer to struggle with image detection. While the human brain is capable of compensating for the invisible parts of the blocked object, computers lack such scene interpretation skills. Cloud computing using convolutional neural networks is typically the method of choice for handling such a scenario. However, for mobile applications where energy consumption and computational costs are critical, cloud computing should be minimized. In this regard, we propose a computer vision sensor capable of efficiently detecting and tracking covered objects without heavy reliance on occlusion handling software. Our edge-computing sensor accomplishes this task by self-learning the object prior to the moment of occlusion and uses this information to “reconstruct” the blocked invisible features. Furthermore, the sensor is capable of tracking a moving object by predicting the path it will most likely take while travelling out of sight behind an obstructing body. Finally, sensor operation is demonstrated by exposing the device to various simulated occlusion events. Keywords: Computer vision, occlusion handling, edge computing, object tracking, dye sensitized solar cell. Corresponding author Email: [email protected]
Many species can dynamically alter their skin textures to enhance their motility and survivability. Despite the enormous efforts on designing bio-inspired materials with tunable surface textures, developing spatiotemporally programmable and reconfigurable textural morphing without complex control remains challenging. Here we propose a design strategy to achieve metasurfaces with such properties. The metasurfaces comprise an array of unit cells with broadly tailored temporal responses. By arranging the unit cells differently, the metasurfaces can exhibit various spatiotemporal responses, which can be easily reconfigured by disassembling and rearranging the unit cells. Specifically, we adopt viscoelastic shells as the unit cells, which can be pneumatically actuated to a concave state, and recover the initial convex state some time after the load is removed. We computationally and experimentally show that the recovery time can be widely tuned by the geometry and material viscoelasticity of the shells. By assembling such shells with different recovery time, we build metasurfaces with pre-programmed spatiotemporal textural morphing under simple pneumatic actuation, and demonstrate temporal evolution of patterns, such as digit numbers and emoji, and spatiotemporal control of friction. This work opens up new avenues in designing spatiotemporal morphing metasurfaces that could be employed for programming mechanical, optical and electrical properties. Corresponding author: Lihua Jin, Email: [email protected]