Advanced intelligent systems (Weinheim an der Bergstrasse, Germany)最新文献

The newly suggested synapse capacitor (synaptor) in this work has a cross-point feature, enabling implementation at a feature size of 4F². This synaptor has a gate surrounding semiconductor pillar (GSSP) structure with overturned charge injection (OCI) scheme to ensure high capacitive memory window. Sentaurus TCAD simulation tools are used to demonstrate the process feasibility and device characteristics. Two important process parameters are optimized to show the best characteristics; overlap height (H_ov) and channel pillar height (H_ch). An OCI-GSSP device that has an aspect ratio of 10 and the minimal overlap height shows the highest C_on/C_off over 5 in 40 nm wordline and BL pitch. It is the highest value and the smallest unit device size among the capacitive synapses that have been reported up to now. Advantages of scaled OCI-GSSP devices are appealed through subarray circuit simulation. The subarray composed of OCI-GSSP synaptor can calculate one vector-matrix multiplication operation with energy under 200 fJ and column delay of 3 ns, and result in sufficient signal margin of 275 mV.

Magnetic field generators are a key component of Magnetic Particle Imaging (MPI) systems, and their power consumption is a major obstacle on the path to human-sized scanners. Despite their importance, a focused discussion of these generators is rare, and a comprehensive description of the design process is currently lacking. This work presents a methodology for the design and optimization of selection field generators operating with soft magnetic materials outside the linear regime in the context of MPI. Key elements are a mathematical model of magnetic field generators, a formalism for defining field sequences, and a relationship between power consumption and field sequence. These are used to define the design space of a field generator given its system requirements and constraints. The design process is then formulated as an optimization problem. Subsequently, this methodology is then utilized to design a new magnetic field generator specifically for cerebral imaging studies. The optimization result outperforms our existing MPI field generator in terms of power consumption and field of view size, providing a proof-of-concept for the entire methodology. As the approach is very general, it can be extended beyond the MPI context to other areas such as magnetic manipulation of medical devices and micro-robotics.

Bioinspired Robotic Fingers

In article number 2400175, Juntian Qu and co-workers report a bio-inspired robotic finger for multi-modal tactile sensing. Inspired by the tactile perception mechanisms of various organisms, this work integrates distributed fiber optic sensing technology to propose a multimodal tactile sensing soft robotic finger with bio-inspired whisker and hair-like structures. It can perceive various parameters such as touch state, contact force, surface roughness, object hardness, and contact position. Additionally, it is capable of dexterously and non-destructively grasping fragile objects and underwater transparent objects.

Fluid-Driven Director-Field Design

In article number 2400179, Yaron Veksler and co-workers present a modular platform of interconnected multi-stable tubes that can transform into a wide range of desired shapes on demand. Using detachable links designed based on director-field theory and viscous fluid actuation, they easily control the shape morphing process. This enables dramatic changes in the final shape while unlocking numerous intermediate configurations. Their method opens new possibilities for deployable structures in applications ranging from soft robotics to medical devices and space exploration.

Kirigami Flexible Grippers

A kirigami multi-stable flexible gripper with trigger structure is presented by Xiuting Sun and co-workers in article number 2400038. The advanced trigger structure is designed to make the flexible gripper enable an energy-free switching behavior between deployed and curled configurations in symmetrical and asymmetrical planes. The proposed gripper is appropriate for the capture of space debris and demonstrates significant capture capability for moving targets.

Throughout evolution, plants have developed a number of strategies to induce movement ensuring the survival and reproduction of their species. Carnivorous plants feed by trapping insects, sunflowers grow by following the sun, and pine cones control the dispersal of their seeds under optimal conditions. This comprehensive review first analyses the mechanisms responsible for these adaptive movements highlighting the need for a gradient in one of the physico-chemical properties to enable morphing. Next, the main synthetic shape-changing soft systems are presented in terms of their mechanism of action. A gradient in water-swelling, magneto-rheological properties, residual deformation, or extensibility gives hygromorphs, magnetomorphs, mechanomorphs, and baromorphs, respectively. The various morphing triggers, namely light, heat, pH, tensile stress, pressure, electric, and magnetic fields, are described. The opportunities and challenges of each system are put into perspective. This report aims to provide the reader with a toolbox for designing shape-changing materials considering the targeted trigger, as well as providing a deeper understanding of the underlying mechanisms paving the way for future discoveries.

Biomimetic Synthesis

In article 2300467, Weiqi Fu and co-workers use machine learning techniques to design peptides with silicifying functionality. Inspired by the exquisite nanosilica structures from nature, a deep learning model, based on antimicrobial peptide migration learning, is developed with the inputs of a comprehensive collection of silicifying peptides from diatoms to achieve the biomimetic synthesis of nanosilica. The newly designed silicified peptides could facilitate the development of new biosensors and drug delivery systems. [Image by Jiwei Chen and Mengsheng Xia.]

Targeted Mass Spectrometry Data Analysis

The application of liquid chromatography-tandem mass spectrometry (LC-MS/MS) has facilitated the earlier detection and diagnosis of diseases preceding the manifestation of symptoms, but data analysis is complicated for clinical application. Integrating an automated machine learning pipeline can optimize LC-MS/MS data processing and analysis, even with limited training datasets. Machine learning pipelines can also implement an active learning nested model to mitigate bias from imbalanced training datasets, providing more accurate clinical proteomic analysis and disease diagnostic results. For more details, refer to article number 2300773 by Jia Fan, Duran Bao, and co-workers.

Artificial intelligence (AI) and smartphones have attracted significant interest in microfluidic paper-based colorimetric sensing due to their convenience and robustness. Recently, AI-based classification of colorimetric assays has been increasingly reported. However, quantitative evaluation remains a challenge, as classification aims to categorize the color change into discrete class labels rather than a quantity. Therefore, in this study, an AI-based regression model with enhanced accuracy is developed and integrated into a microfluidic paper-based analytical device for simultaneous colorimetric measurements of glucose, cholesterol, and pH. The model is also embedded into a smartphone via a custom-designed Android application named ChemiCheck to complete on-site colorimetric quantification without internet access in under 1 s. The results demonstrate that the integrated system is able to sensitively detect both glucose (limit of detection [LOD]: 131 $��\mu �M�$) and cholesterol (LOD: 217 $��\mu �M�$), concluding the entire analysis within minutes while maintaining a maximum root mean square error of 0.386. Overall, the integrated platform holds great promise for point-of-care testing and offers numerous advantages, including easy-to-use operation, rapid response, low-cost, high selectivity, and consistent repeatability, particularly in nonlaboratory and resource-limited environments.

Particle-jamming soft grippers demonstrate notable shape adaptability and adjustable stiffness, which improve their grasping efficiency. However, integrating tactile sensing into these grippers presents challenges due to the specific properties of the particle jamming mechanism. This study introduces a parallel particle jamming soft gripper equipped with tactile sensing capabilities. The gripper consists of two tactile sensing particle jamming pads (TSPJPs) that are integrated with flexible optoelectronic skins. These skins are made of silicone rubber membranes and are embedded with a 3 × 3 array of stretchable optical waveguide arrays (SOWAs). Testing indicates that incorporating these sensors enhances the gripper's tactile sensing capabilities, with minimal impact on its particle jamming-based grasping function. A single TSPJP can accurately detect various contact points and estimate the contract forces. The proposed soft gripper can reliably grasp a wide range of objects, varying in shape, hardness, and weight, and it provides detailed tactile feedback on contact locations and the intensity of the grasping through the SOWA sensor. It can precisely distinguish between different grasping postures using a light gradient boosting machine (LightGBM) learning model. Furthermore, it can effectively detect the slippage of grasped objects, facilitating accurate closed-loop control for secure manipulation.