Group14 Technologies, Inc.

Amazon Technologies, Inc.

Basf Se Corporation

Georgia-pacific Chemicals LLC

Energ2 Technologies, Inc.

Polydrop, LLC

Leah A Thompkins

Henry R Costantino

Aaron M Feaver

Avery J Sakshaug

Benjamin E Kron

Katharine Geramita

Alan Tzu-Yang Chang

Farshid Afkhami

Adam Strong

Sarah Fredrick

Chad Goodwin

Liam Cover

Cory Mekelburg

John B Hines

Shahid P Qureshi

Joseph Frank Ludvik

Christopher Timmons

Xing Dong

Gerald A Knazek

Anthony Sharma

Jarrod Sherwin

Mehran Kafai

Minghao Gai

Renette E Richard

Zheshen Wang

Lilo Danielle Pozzo

Dimitris Papadimitriou

Muge Erdirik Dogan

Arun Kumar Galba

Zhiyuan Cao

Kelly Anne Nigh

Husam Saqallah

Kaspar Kenneth Mueller

Matthew J Maroon

Chengeng Zeng

Carinna Simmons

Volha Hrechka

Brian Panganiban

The present application is directed to methods for preparation of carbon materials. The carbon materials comprise enhanced electrochemical properties and find utility in any number of electrical devices, for example, as electrode material in ultracapacitors or batteries.

Preparation of polymeric resins and carbon materials

The present application is generally directed to composites comprising a hard carbon material and an electrochemical modifier. The composite materials find utility in any number of electrical devices, for example, in lithium ion batteries. Methods for making the disclosed composite materials are also disclosed.

Composite carbon materials comprising lithium alloying electrochemical modifiers

The present application is directed to methods for solvent-free preparation of polymers and their subsequent processing into activated carbon materials. These methods unexpectedly demonstrate ability to tune pore structure in the polymer gel and carbon produced there from, while also providing distinct advantages over the current art.

Methods for sol-gel polymerization in absence of solvent and creation of tunable carbon structure from same

A system can be configured to receive and analyze an image captured by a customer at a delivery location to enable identification and verification of the delivery location upon arrival of a deliverer at the delivery location. The system can utilize algorithms trained to determine identifying features of the delivery location from the image and/or provide the image to the deliverer for manual verification. The system can provide the deliverer with the image and additional information specific to the delivery location based on one or more geofences associated with the delivery location. The image, the additional information and the manner of presentation can be configured to guide the deliverer to approved drop-off locations and ensure package placement for delivery to a customer.

Location-based package visual delivery instructions

The present application is generally directed to gas storage materials such as activated carbon comprising enhanced gas adsorption properties. The gas storage materials find utility in any number of gas storage applications. Methods for making the gas storage materials are also disclosed.

Carbon-based compositions with highly efficient volumetric gas sorption

Porous silicon and methods for preparation and use of the same are disclosed. The porous silicon materials have utility either alone or in combination with other materials, for example, combined with carbon particles for energy storage applications.

Nano-featured porous silicon materials

User information may be used to create a training exercise representing simulated package delivery. The user information can include metrics corresponding to physical package delivery. The user information may be used as part of a predictive model to determine the content of the training exercise, including the type and number of tasks comprising the exercise. Once created, the training exercise can be presented to a user as a graphical simulation. The presentation can occur in response to one or more triggering conditions.

Dynamic virtual environment for improved situational awareness

A logistics system for managing a network of independent delivery partners may include models to detect delivery defects. The models may generate defect scores for each onboarded delivery partner. The system may collect delivery data to continuously update the models. The updated models may score delivery partners for early detection of delivery defects and determine remediations steps. The system may train one or more models to determine the scores based on a terminating step of the delivery steps. The scores may indicate a likelihood the terminating step was caused by intentional action or by accident. The system may determine remediations steps based on the likelihood of intentional action.

Models for early detection of delivery defects&#x2014;unassigned delivery blocks

Models for early detection of delivery defects&#x2014;expired delivery blocks

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Seattle, WA, United States of America

Leah A Thompkins