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A study conducted by researchers at the U.S. Department of Energy s (DOE) Argonne National Laboratory reveals that the use of corn ethanol is reducing the carbon footprint and diminishing greenhouse gases.
The study, recently published in
Biofuels, Bioproducts and Biorefining, analyzes corn ethanol production in the United States from 2005 to 2019, when production more than quadrupled. Scientists assessed corn ethanol s greenhouse gas (GHG) emission intensity (sometimes known as carbon intensity, or CI) during that period and found a 23% reduction in CI.
According to Argonne scientists, corn ethanol production increased over the period, from 1.6 to 15 billion gallons (6.1 to 57 billion liters). Supportive biofuel policies such as the Environmental Protection Agency s Renewable Fuel Standard and California s Low-Carbon Fuel Standard helped generate the increase. Both of those federal and state programs evaluate the life-cycle GHG emissions of fuel production pathway
NREL: New Combustion Strategies Plus Biofuels Add Up to Cleaner, More Efficient Cars and Trucks
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USABC PICKING WINNERS AWARDS $7.5 MILLION PRE-LITHIATION EV BATTERY CELL TECHNOLOGY DEVELOPMENT CONTRACT TO APPLIED MATERIALS
SOUTHFIELD, Mich., May 19, 2021 – The United States Advanced Battery Consortium LLC (USABC), a subsidiary of the United States Council for Automotive Research LLC (USCAR), today announced the award of a $7.5 million technology development contract to Applied Materials, Inc. in Santa Clara, California for an advanced lithium-ion battery based on pre-lithiated silicon-graphite technology for electric vehicles (EV).
The contract award, which includes a 51.2 percent cost share, funds a 24-month project that began earlier this year. The program’s goal is to develop a roll-to-roll (R2R) pre-lithiation system for high-volume manufacturing. As part of the program, Applied Materials will work with battery manufacturers to demonstrate the technology for use in EVs.
Prototype PNNL, UW webapp predicts parking space availability for delivery drivers
A collaboration between Pacific Northwest National Laboratory (PNNL) and the University of Washington’s Urban Freight Lab has developed a prototype webapp that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find open spaces without expending fuel and losing time.
The webapp combines curbside maps with data from nearly 300 sensors placed within 74 parking spaces in commercial and passenger loading zones from 1
st to 3
rd avenues and between Battery and Stewart streets in the Belltown neighborhood in downtown Seattle.
Date Time
From Curb to Doorstep: Driving Efficiencies for Delivering Goods
Ten blocks of the Belltown neighborhood in downtown Seattle house an eclectic mix of retail stores, apartments, and restaurants that require daily deliveries of goods-from fresh seafood for the evening’s dinner special to the latest gadget from Amazon Prime.
In this bustling and often congested urban area, trucks fully packed with goods are on tight delivery schedules. However, circling the blocks to find an open parking zone in the “final 50 feet”-the sweet spot for the most efficient deliveries-can put drivers behind schedule.
Soon, there will be a webapp for that. In a collaboration between Pacific Northwest National Laboratory (PNNL) and the University of Washington’s Urban Freight Lab, a prototype webapp has been developed that combines smart sensors and machine learning to predict parking space availability. The prototype is ready for initial testing to help commercial delivery drivers find o
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