Lawrence Livermore National Security, LLC

Purdue Research Foundation

Congwang Ye

Eric B Duoss

Sarah Elyse Baker

Joshuah K Stolaroff

Du Nguyen

William Smith

Roger D Aines

James S Oakdale

William L Bourcier

Marcus A Worsley

Joshua D Kuntz

Andrew J Pascall

Brian Giera

Elaine Lee

Megan M Smith

Sachin Subhash Talathi

Aaron Creighton Wilson

Caitlyn Christian Cook

Carlos J Martinez

Jennifer Marie Knipe

Maxwell R Murialdo

Julie A Mancini

Maira R Cerón Hernández

Kevin Scott Paulsen

Fabrication of functional polymer-based particles by crosslinking UV-curable polymer drops in mid-air and collecting crosslinked particles in a solid container, a liquid suspension, or an air flow. The particles can contain different phases in the form or layered structures that contain one to multiple cores, or structures that are blended with dissolved or emulsified smaller domains. A curing system produces ultraviolet rays that are directed onto the particles in the jet stream from one side. A reflector positioned on other side of the jet stream reflects the ultraviolet rays back onto the particles in the jet stream.

Fabricating structured particles through rapid hardening and tailored collection methods

According to one embodiment, a microcapsule for selective catalysis of gases, the microcapsule comprising: a polymeric shell permeable to one or more target gases; and at least one biocatalyst disposed in an interior of the polymeric shell. In more embodiments, methods of forming such microcapsules include: emulsifying at least one biocatalyst in a polymer precursor mixture; emulsifying the polymer precursor mixture in an aqueous carrier solution; crosslinking one or more polymer precursors of the polymer precursor mixture to form a plurality of microcapsules each independently comprising: a polymeric shell permeable to one or more target gases; and at least one biocatalyst disposed in an interior of the polymeric shell. In further embodiments, corresponding methods of using the inventive microcapsules for catalyzing one or more target gases using include: exposing a plurality of the biocatalytic microcapsules to the one or more target gases.

Biocatalytic microcapsules for catalyzing gas conversion

The present disclosure relates to a nozzle system for use in a microfluidic production application for producing at least one of particles, capsules or fibers. The system has a main body portion having a compressed fluid inlet and a core fluid inlet, and a plurality of parallel arranged core fluid nozzles that receive the core fluid and create a plurality of core fluid streams. At least one compressed fluid inlet associated with the main body channels compressed fluid to areas adjacent ends of the core fluid nozzles. An apertured plate having a plurality of apertures is arranged near the ends of the core fluid nozzles, with each aperture being uniquely associated with a single one of the core fluid nozzles. The compressed fluid acts on the core fluid streams exiting the core fluid nozzles to help create, with the apertures, at least one of core fluid droplets or core fluid fibers from the core fluid streams.

Parallelized multiple nozzle system and method to produce layered droplets and fibers for microencapsulation

A composite material for gas capture including COcapture and capture of other gases. The composite material includes solid or liquid reactive material, filler material, and a gas-permeable polymer coating such that the reactive material forms micron-scale domains in the filler material.

Composite 3D-printed reactors for gas absorption, purification, and reaction

A composite material for gas capture, notably COcapture and storage. The composite material includes a mixture of a solid or liquid reactive filler and a gas-permeable polymer such that the reactive filler forms micron-scale domains in the polymer matrix.

A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Automated control of microfluidic devices based on machine learning

Growing community of inventors

Livermore, CA, United States of America

Congwang Ye