Acoustoelectronic nanotweezers enable dynamic and large-scale control of nanomaterials

AbstractThe ability to precisely manipulate nano-objects on a large scale can enable the fabrication of materials and devices with tunable optical, electromagnetic, and mechanical properties. However, the dynamic, parallel manipulation of nanoscale colloids and materials remains a significant challenge. Here, we demonstrate acoustoelectronic nanotweezers, which combine the precision and robustness afforded by electronic tweezers with versatility and large-field dynamic control granted by acoustic tweezing techniques, to enable the massively parallel manipulation of sub-100 nm objects with excellent versatility and controllability. Using this approach, we demonstrated the complex patterning of various nanoparticles (e.g., DNAs, exosomes, ~3 nm graphene flakes, ~6 nm quantum dots, ~3.5 nm proteins, and ~1.4 nm dextran), fabricated macroscopic materials with nano-textures, and performed high-resolution, single nanoparticle manipulation. Various nanomanipulation functions, including transportation, concentration, orientation, pattern-overlaying, and sorting, have also been achieved using a simple device configuration. Altogether, acoustoelectronic nanotweezers overcome existing limitations in nano-manipulation and hold great potential for a variety of applications in the fields of electronics, optics, condensed matter physics, metamaterials, and biomedicine.

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Some Results on Stabilizability of Large-Scale Control Systems with Indetrminancy

1992 American Control Conference ◽

10.23919/acc.1992.4792764 ◽

1992 ◽

Author(s):

A.S.C Sinha

Keyword(s):

Control Systems ◽

Large Scale ◽

Scale Control

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Neural network assisted large-scale structural analysis/reanalysis in a massively parallel environment

10.2514/6.1994-1512 ◽

1994 ◽

Cited By ~ 1

Author(s):

R. Shieh

Keyword(s):

Neural Network ◽

Structural Analysis ◽

Large Scale ◽

Massively Parallel ◽

Parallel Environment

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Massively parallel architectures for large scale neural network simulations

IEEE Transactions on Neural Networks ◽

10.1109/72.165590 ◽

1992 ◽

Vol 3 (6) ◽

pp. 876-888 ◽

Cited By ~ 20

Author(s):

Y. Fujimoto ◽

N. Fukuda ◽

T. Akabane

Keyword(s):

Neural Network ◽

Large Scale ◽

Parallel Architectures ◽

Massively Parallel ◽

Massively Parallel Architectures ◽

Network Simulations

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Crack Growth and Propagation in Metallic Alloys

MRS Proceedings ◽

10.1557/proc-409-81 ◽

1995 ◽

Vol 409 ◽

Author(s):

W. C. Morrey ◽

L. T. Wille

Keyword(s):

Large Scale ◽

Critical Strain ◽

Cleavage Plane ◽

Crack Tip ◽

Metallic Alloys ◽

Propagation Direction ◽

Dynamics Simulation ◽

Parallel Computer ◽

Massively Parallel ◽

Tip Position

AbstractUsing large-scale molecular dynamics simulation on a massively parallel computer, we have studied the initiation of cracking in a Monel-like alloy of Cu-Ni. In a low temperature 2D sample, fracture from a notch starts at a little beyond 2.5% critical strain when the propagation direction is perpendicular to a cleavage plane. We discuss a method of characterizing crack tip position using a measure of area around the crack tip.

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Comparison of Various Discretization Schemes for Simulation of Large Field Case Reservoirs Using Unstructured Grids

10.2118/203949-ms ◽

2021 ◽

Author(s):

Samier Pierre ◽

Raguenel Margaux ◽

Darche Gilles

Keyword(s):

Large Scale ◽

Unstructured Grids ◽

Computational Cost ◽

Large Field ◽

Real Field ◽

Test Case ◽

Generation Task ◽

Field Case ◽

Flux Approximation ◽

Discretization Schemes

Abstract Solving the equations governing multiphase flow in geological formations involves the generation of a mesh that faithfully represents the structure of the porous medium. This challenging mesh generation task can be greatly simplified by the use of unstructured (tetrahedral) grids that conform to the complex geometric features present in the subsurface. However, running a million-cell simulation problem using an unstructured grid on a real, faulted field case remains a challenge for two main reasons. First, the workflow typically used to construct and run the simulation problems has been developed for structured grids and needs to be adapted to the unstructured case. Second, the use of unstructured grids that do not satisfy the K-orthogonality property may require advanced numerical schemes that preserve the accuracy of the results and reduce potential grid orientation effects. These two challenges are at the center of the present paper. We describe in detail the steps of our workflow to prepare and run a large-scale unstructured simulation of a real field case with faults. We perform the simulation using four different discretization schemes, including the cell-centered Two-Point and Multi-Point Flux Approximation (respectively, TPFA and MPFA) schemes, the cell- and vertex-centered Vertex Approximate Gradient (VAG) scheme, and the cell- and face-centered hybrid Mimetic Finite Difference (MFD) scheme. We compare the results in terms of accuracy, robustness, and computational cost to determine which scheme offers the best compromise for the test case considered here.

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Beyond spatial scalability limitations with a massively parallel method for linear oscillatory problems

The International Journal of High Performance Computing Applications ◽

10.1177/1094342016687625 ◽

2017 ◽

Vol 32 (6) ◽

pp. 913-933 ◽

Cited By ~ 6

Author(s):

Martin Schreiber ◽

Pedro S Peixoto ◽

Terry Haut ◽

Beth Wingate

Keyword(s):

High Performance ◽

Large Scale ◽

Weather Prediction ◽

Finite Difference Methods ◽

Scaling Limit ◽

Performance Model ◽

Massively Parallel ◽

Large Set ◽

Problem Size ◽

Single Node

This paper presents, discusses and analyses a massively parallel-in-time solver for linear oscillatory partial differential equations, which is a key numerical component for evolving weather, ocean, climate and seismic models. The time parallelization in this solver allows us to significantly exceed the computing resources used by parallelization-in-space methods and results in a correspondingly significantly reduced wall-clock time. One of the major difficulties of achieving Exascale performance for weather prediction is that the strong scaling limit – the parallel performance for a fixed problem size with an increasing number of processors – saturates. A main avenue to circumvent this problem is to introduce new numerical techniques that take advantage of time parallelism. In this paper, we use a time-parallel approximation that retains the frequency information of oscillatory problems. This approximation is based on (a) reformulating the original problem into a large set of independent terms and (b) solving each of these terms independently of each other which can now be accomplished on a large number of high-performance computing resources. Our results are conducted on up to 3586 cores for problem sizes with the parallelization-in-space scalability limited already on a single node. We gain significant reductions in the time-to-solution of 118.3× for spectral methods and 1503.0× for finite-difference methods with the parallelization-in-time approach. A developed and calibrated performance model gives the scalability limitations a priori for this new approach and allows us to extrapolate the performance of the method towards large-scale systems. This work has the potential to contribute as a basic building block of parallelization-in-time approaches, with possible major implications in applied areas modelling oscillatory dominated problems.

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Supercomputer simulations of structure formation in the Universe

Proceedings of the International Astronomical Union ◽

10.1017/s174392131700045x ◽

2016 ◽

Vol 12 (S325) ◽

pp. 10-16

Author(s):

Tomoaki Ishiyama

Keyword(s):

Domain Decomposition ◽

Structure Formation ◽

Short Range ◽

Large Scale ◽

Massively Parallel ◽

Average Performance ◽

And Performance ◽

Performance Results ◽

The Universe ◽

Peak Speed

AbstractWe describe the implementation and performance results of our massively parallel MPI†/OpenMP‡ hybrid TreePM code for large-scale cosmological N-body simulations. For domain decomposition, a recursive multi-section algorithm is used and the size of domains are automatically set so that the total calculation time is the same for all processes. We developed a highly-tuned gravity kernel for short-range forces, and a novel communication algorithm for long-range forces. For two trillion particles benchmark simulation, the average performance on the fullsystem of K computer (82,944 nodes, the total number of core is 663,552) is 5.8 Pflops, which corresponds to 55% of the peak speed.

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Assessing the measurement transfer function of single-cell RNA sequencing

10.1101/045450 ◽

2016 ◽

Author(s):

Hannah R. Dueck ◽

Rizi Ai ◽

Adrian Camarena ◽

Bo Ding ◽

Reymundo Dominguez ◽

...

Keyword(s):

Transfer Function ◽

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Transfer Functions ◽

Single Cells ◽

Biological Information ◽

Gene Detection ◽

Single Cell Rna Sequencing ◽

Scale Control

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.

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