Recent progress in the design of photocatalytic H2O2 synthesis system

Photocatalytic synthesis of hydrogen peroxide under mild reaction conditions is a promising technology. This article will review the recent research progress in the design of photocatalytic H2O2 synthesis systems. A comprehensive discussion of the strategies that could solve two essential issues related to H2O2 synthesis. That is, how to improve the reaction kinetics of H2O2 formation via 2e− oxygen reduction reaction and inhibit the H2O2 decomposition through a variety of surface functionalization methods. The photocatalyst design and the reaction mechanism will be especially stressed in this work which will be concluded with an outlook to show the possible ways for synthesizing high-concentration H2O2 solution in the future

Fuel and Emissions Calculator (FEC) Version 2.0

The Fuel and Emissions Calculator (FEC) is an operating-mode-based, life-cycle emissions modeling tool developed by the Georgia Institute of Technology researchers. The primary purpose of the FEC is to assist fleet owners and managers, regulatory agencies, and policy analysts in assessing the energy and emissions impacts of fleet alternatives. The FEC\u2019s modeling approach estimates emissions as a function of engine load, which in turn is a function of vehicle service parameters, allowing modelers to account for local on-road operating mode conditions as model inputs. The functional modules are embedded in an Excel spreadsheet platform for all current model versions. The open platform allows users to see all input data and every calculation, which makes the model transparent and accessible for most users. With Version 2.0 of the model, an online Python version of the model has also been developed. The Python version enhances model performance, and provides functionality for advanced users who may wish to link the FEC with other modeling tools, such as travel demand or simulation models. The first Fuel and Emissions Calculator (Version 1.0), known as \u2018FEC for transit fleets,\u2019 was originally developed by Georgia Tech researchers in 2013-2014 for transit bus, shuttle bus and rail systems (ORNL and Georgia Tech, 2014). This report first summarizes the FEC Version 2.0 model\u2019s main features. The generic methodology that is applied to all transportation modes is introduced in Chapter 2, which includes modules for scenario setting, energy consumption, on road emission rates, life-cycle assessment, and cost-effectiveness. The model specifications for individual transportation modes are introduced in Chapter 3, and case study examples are provided to help users prepare customized analysis for their own fleets. The key considerations for establishing the online FEC are discussed in Chapter 4. Current research achievements and ongoing work to update and improve the FEC are provided in the final Chapter

Medaka vasa gene has an exonic enhancer for germline expression

10.1016/j.gene.2014.11.039Gene5552403-40

Fuel and Emissions Calculator (FEC) Version 2.0

The Fuel and Emissions Calculator (FEC) is an operating-mode-based, life-cycle emissions modeling tool developed by the Georgia Institute of Technology researchers. The primary purpose of the FEC is to assist fleet owners and managers, regulatory agencies, and policy analysts in assessing the energy and emissions impacts of fleet alternatives. The FEC’s modeling approach estimates emissions as a function of engine load, which in turn is a function of vehicle service parameters, allowing modelers to account for local on-road operating mode conditions as model inputs. The functional modules are embedded in an Excel spreadsheet platform for all current model versions. The open platform allows users to see all input data and every calculation, which makes the model transparent and accessible for most users. With Version 2.0 of the model, an online Python version of the model has also been developed. The Python version enhances model performance, and provides functionality for advanced users who may wish to link the FEC with other modeling tools, such as travel demand or simulation models. The first Fuel and Emissions Calculator (Version 1.0), known as ‘FEC for transit fleets,’ was originally developed by Georgia Tech researchers in 2013-2014 for transit bus, shuttle bus and rail systems (ORNL and Georgia Tech, 2014). This report first summarizes the FEC Version 2.0 model’s main features. The generic methodology that is applied to all transportation modes is introduced in Chapter 2, which includes modules for scenario setting, energy consumption, onroad emission rates, life-cycle assessment, and cost-effectiveness. The model specifications for individual transportation modes are introduced in Chapter 3, and case study examples are provided to help users prepare customized analysis for their own fleets. The key considerations for establishing the online FEC are discussed in Chapter 4. Current research achievements and ongoing work to update and improve the FEC are provided in the final Chapter.View the NCST Project Webpag

Georgia Express Lane Corridors Vehicle Occupancy and Throughput Study 2018-2020 - Volume I: Vehicle and Person Throughput Analysis Before and After the I-75 Northwest Corridor and I-85 Express Lanes Extension

Ongoing assessment of system performance monitoring is critical to successful and efficient transportation planning, ensuring that infrastructure investments provide a desired return on investment. As with any new transportation facility, it is important to understand how Express Lane facilities affect travel behavior, resulting on-road vehicle activity, and subsequent person-throughput (a function of vehicle occupancy) as part of the facility performance assessment. This report summarizes the vehicle and person throughput analysis for the I-75 Northwest Corridor (NWC) and I-85 Express Lanes in Atlanta, GA, undertaken by the Georgia Institute of Technology research team for the State Road and Tollway Authority (SRTA). The research team tracked changes in observed vehicle throughput on four managed lane corridors and collected vehicle occupancy (persons per vehicle) data to assess changes in both vehicle throughput and person throughput associated with the opening of new Express Lane facilities. The team collected traffic volumes by video observation (GDOT’s Georgia NaviGAtor machine vision system and SRTA’s vehicle activity monitoring system). The team implemented a large-scale data collection effort for vehicle occupancy across all general purpose freeway lanes and from SRTA’s Express Lanes over a two-year period (before-and-after the opening of the Express Lanes). Between the baseline year (2018) and post-opening year (2019), the team observed a decrease in average vehicle occupancy (persons/vehicle), coupled with a significant increase in traffic volumes, especially on the NWC. The combined effect of increased traffic volumes and decreased occupancy still led to an overall increase in person throughput at all sites. Vehicle throughput on the I-85 corridor increased by about 5-7% and person throughput increased by 1-2% in the morning peak, and increased by around 10% for vehicles and 5% for persons in the evening peak. Vehicle throughput increased by more than 35% on I-575 in the AM and PM peaks, and by the same on I-75 in the AM peaks (only minor increases were noted in the PM peaks), likely due to the diversion of commute traffic from arterials onto the freeway corridor once the Express Lanes opened and congestion declined. Based upon vehicle throughput and occupancy distributions, the largest share of the increase in vehicle throughput in the peak periods came from an influx of single-occupant vehicle activity onto the corridor. Even though the number of carpools traversing the I-575 corridor increased slightly during the morning peak, the overall carpool mode share (percentage of carpools) decreased after the significantly greater numbers of single-occupant vehicles began using the corridor.View the NCST Project Webpag

Dynamic Anti-Counterfeiting Labels with Enhanced Multi-Level Information Encryption

Information encryption is an important means to improve the security of anti-counterfeiting labels. At present, it is still challenging to realize an anti-counterfeiting label with multi-function, high security factor, low production cost, and easy detection and identification. Herein, using inkjet and screen printing technology, we construct a multi-dimensional and multi-level dynamic optical anti-counterfeiting label based on instantaneously luminescent quantum dots and long afterglow phosphor, whose color and luminous intensity varied in response to time. Self-assembled quantum dot patterns with intrinsic fingerprint information endow the label with physical unclonable functions (PUFs), and the information encryption level of the label is significantly improved in view of the information variation in the temporal dimension. Furthermore, the convolutional residual neural networks are used to decode the massive information of PUFs, enabling fast and accurate identification of the anti-counterfeit labels