Conventional assembly of biosystems has relied on bottom‐up techniques, such as directed aggregation, or top‐down techniques, such as layer‐by‐layer integration, using advanced lithographic and additive manufacturing processes. However, these methods often fail to mimic the complex three dimensional (3D) microstructure of naturally occurring biomachinery, cells, and organisms regarding assembly throughput, precision, material heterogeneity, and resolution. Pop‐up, buckling, and self‐folding methods, reminiscent of paper origami, allow the high‐throughput assembly of static or reconfigurable biosystems of relevance to biosensors, biomicrofluidics, cell and tissue engineering, drug delivery, and minimally invasive surgery. The universal principle in these assembly methods is the engineering of intrinsic or extrinsic forces to cause local or global shape changes via bending, curving, or folding resulting in the final 3D structure. The forces can result from stresses that are engineered either during or applied externally after synthesis or fabrication. The methods facilitate the high‐throughput assembly of biosystems in simultaneously micro or nanopatterned and layered geometries that can be challenging if not impossible to assemble by alternate methods. The authors classify methods based on length scale and biologically relevant applications; examples of significant advances and future challenges are highlighted.
Establishing Digital Recognition and Identification of Microscopic Objects for Implementation of Artificial Intelligence (AI) Guided Microassembly
As miniaturization of electrical and mechanical components used in modern technology progresses, there is an increasing need for high-throughput and low-cost micro-scale assembly techniques. Many current micro-assembly methods are serial in nature, resulting in unfeasibly low throughput. Additionally, the need for increasingly smaller tools to pick and place individual microparts makes these methods cost prohibitive. Alternatively, parallel self-assembly or directed-assembly techniques can be employed by utilizing forces dominant at the micro and nano scales such as electro-kinetic, thermal, and capillary forces. However, these forces are governed by complex equations and often act on microparts simultaneously and competitively, making modeling and simulation difficult. The research in this paper presents a novel phenomenological approach to directed micro-assembly through the use of artificial intelligence to correlate micro-particle movement via dielectrophoretic and electro-osmotic forces in response to varying frequency of an applied non-uniform electric field. This research serves as a proof of concept of the application of artificial intelligence to create high yield low-cost micro-assembly techniques, which will prove useful in a variety of fields including micro-electrical-mechanical systems (MEMS), biotechnology, and tissue engineering.
more »
« less
- Award ID(s):
- 1661877
- NSF-PAR ID:
- 10313499
- Date Published:
- Journal Name:
- Proceedings of World Congress on Micro and Nano Manufacturing 2021
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
more » « less
Abstract -
Colloidal nanocrystals (NCs) have emerged as a diverse class of materials with tunable composition, size, shape, and surface chemistry. From their facile syntheses to unique optoelectronic properties, these solution-processed nanomaterials are a promising alternative to materials grown as bulk crystals or by vapor-phase methods. However, the integration of colloidal nanomaterials in real-world devices is held back by challenges in making patterned NC films with the resolution, throughput, and cost demanded by device components and applications. Therefore, suitable approaches to pattern NCs need to be established to aid the transition from individual proof-of-concept NC devices to integrated and multiplexed technological systems. In this Account, we discuss the development of stimuli-sensitive surface ligands that enable NCs to be patterned directly with good pattern fidelity while retaining desirable properties. We focus on rationally selected ligands that enable changes in the NC dispersibility by responding to light, electron beam, and/or heat. First, we summarize the fundamental forces between colloidal NCs and discuss the principles behind NC stabilization/destabilization. These principles are applied to understanding the mechanisms of the NC dispersibility change upon stimuli-induced ligand modifications. Six ligand-based patterning mechanisms are introduced: ligand cross-linking, ligand decomposition, ligand desorption, in situ ligand exchange, ion/ligand binding, and ligand-aided increase of ionic strength. We discuss examples of stimuli-sensitive ligands that fall under each mechanism, including their chemical transformations, and address how these ligands are used to pattern either sterically or electrostatically stabilized colloidal NCs. Following that, we explain the rationale behind the exploration of different types of stimuli, as well as the advantages and disadvantages of each stimulus. We then discuss relevant figures-of-merit that should be considered when choosing a particular ligand chemistry or stimulus for patterning NCs. These figures-of-merit pertain to either the pattern quality (e.g., resolution, edge and surface roughness, layer thickness), or to the NC material quality (e.g., photo/electro-luminescence, electrical conductivity, inorganic fraction). We outline the importance of these properties and provide insights on optimizing them. Both the pattern quality and NC quality impact the performance of patterned NC devices such as field-effect transistors, light-emitting diodes, color-conversion pixels, photodetectors, and diffractive optical elements. We also give examples of proof-of-concept patterned NC devices and evaluate their performance. Finally, we provide an outlook on further expanding the chemistry of stimuli-sensitive ligands, improving the NC pattern quality, progress toward 3D printing, and other potential research directions. Ultimately, we hope that the development of a patterning toolbox for NCs will expedite their implementation in a broad range of applications.more » « less
-
Pipeline networks are a crucial component of energy infrastructure, and natural force damage is an inevitable and unpredictable cause of pipeline failures. Such incidents can result in catastrophic losses, including harm to operators, communities, and the environment. Understanding the causes and impact of these failures is critical to preventing future incidents. This study investigates artificial intelligence (AI) algorithms to predict natural gas pipeline failures caused by natural forces, using climate change data that are incorporated into pipeline incident data. The AI algorithms were applied to the publicly available Pipeline and Hazardous Material Safety Administration (PHMSA) dataset from 2010 to 2022 for predicting future patterns. After data pre-processing and feature selection, the proposed model achieved a high prediction accuracy of 92.3% for natural gas pipeline damage caused by natural forces. The AI models can help identify high-risk pipelines and prioritize inspection and maintenance activities, leading to cost savings and improved safety. The predictive capabilities of the models can be leveraged by transportation agencies responsible for pipeline management to prevent pipeline damage, reduce environmental damage, and effectively allocate resources. This study highlights the potential of machine learning techniques in predicting pipeline damage caused by natural forces and underscores the need for further research to enhance our understanding of the complex interactions between climate change and pipeline infrastructure monitoring and maintenance.more » « less
-
more » « less
Abstract Nanoparticles form long‐range micropatterns via self‐assembly or directed self‐assembly with superior mechanical, electrical, optical, magnetic, chemical, and other functional properties for broad applications, such as structural supports, thermal exchangers, optoelectronics, microelectronics, and robotics. The precisely defined particle assembly at the nanoscale with simultaneously scalable patterning at the microscale is indispensable for enabling functionality and improving the performance of devices. This article provides a comprehensive review of nanoparticle assembly formed primarily via the balance of forces at the nanoscale (e.g., van der Waals, colloidal, capillary, convection, and chemical forces) and nanoparticle‐template interactions (e.g., physical confinement, chemical functionalization, additive layer‐upon‐layer). The review commences with a general overview of nanoparticle self‐assembly, with the state‐of‐the‐art literature review and motivation. It subsequently reviews the recent progress in nanoparticle assembly without the presence of surface templates. Manufacturing techniques for surface template fabrication and their influence on nanoparticle assembly efficiency and effectiveness are then explored. The primary focus is the spatial organization and orientational preference of nanoparticles on non‐templated and pre‐templated surfaces in a controlled manner. Moreover, the article discusses broad applications of micropatterned surfaces, encompassing various fields. Finally, the review concludes with a summary of manufacturing methods, their limitations, and future trends in nanoparticle assembly.
-
Generative Adversarial Networks (GANs) have recently drawn tremendous attention in many artificial intelligence (AI) applications including computer vision, speech recognition, and natural language processing. While GANs deliver state-of-the-art performance on these AI tasks, it comes at the cost of high computational complexity. Although recent progress demonstrated the promise of using ReRMA-based Process-In-Memory for acceleration of convolutional neural networks (CNNs) with low energy cost, the unique training process required by GANs makes them difficult to run on existing neural network acceleration platforms: two competing networks are simultaneously co-trained in GANs, and hence, significantly increasing the need of memory and computation resources. In this work, we propose ReGAN – a novel ReRAM-based Process-In-Memory accelerator that can efficiently reduce off-chip memory accesses. Moreover, ReGAN greatly increases system throughput by pipelining the layer-wise computation. Two techniques, namely, Spatial Parallelism and Computation Sharing are particularly proposed to further enhance training efficiency of GANs. Our experimental results show that ReGAN can achieve 240X performance speedup compared to GPU platform averagely, with an average energy saving of 94X.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
