Rolling resistance of electric vehicle tires from track tests

Special low-rolling-resistance tires were made for DOE's ETV-1 electric vehicle. Tests were conducted on these tires and on a set of standard commercial automotive tires to determine the rolling resistance as a function of time during both constant-speed tires and SAE J227a driving cycle tests. The tests were conducted on a test track at ambient temperatures that ranged from 15 to 32 C (59 to 89 F) and with tire pressures of 207 to 276 kPa (30 to 40 psi). At a contained-air temperature of 38 C (100 F) and a pressure of 207 kPa (30 psi) the rolling resistances of the electric vehicle tires and the standard commercial tires, respectively, were 0.0102 and 0.0088 kilogram per kilogram of vehicle weight. At a contained-air temperature of 38 C (100 F) and a pressure of 276 kPa (40 psi) the rolling resistances were 0.009 and 0.0074 kilogram per kilogram of vehicle weight, respectively

Performance of conventionally powered vehicles tested to an electric vehicle test procedure

A conventional Volkswagen transporter, a Renault 5, a Pacer, and a U. S. Postal Service general DJ-5 delivery van were treated to an electric vehicle test procedure in order to allow direct comparison of conventional and electric vehicles. Performance test results for the four vehicles are presented

Baseline tests of the EPC Hummingbird electric passenger vehicle

The rear-mounted internal combustion engine in a four-passenger Volkswagen Thing was replaced with an electric motor made by modifying an aircraft generator and powered by 12 heavy-duty, lead-acid battery modules. Vehicle performance tests were conducted to measure vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability limit, road energy consumption, road power, indicated energy consumption, braking capability, battery charger efficiency, and battery characteristics. Test results are presented in tables and charts

Automation of a Positron-emission Tomography (PET) Radiotracer Synthesis Protocol for Clinical Production.

The development of new positron-emission tomography (PET) tracers is enabling researchers and clinicians to image an increasingly wide array of biological targets and processes. However, the increasing number of different tracers creates challenges for their production at radiopharmacies. While historically it has been practical to dedicate a custom-configured radiosynthesizer and hot cell for the repeated production of each individual tracer, it is becoming necessary to change this workflow. Recent commercial radiosynthesizers based on disposable cassettes/kits for each tracer simplify the production of multiple tracers with one set of equipment by eliminating the need for custom tracer-specific modifications. Furthermore, some of these radiosynthesizers enable the operator to develop and optimize their own synthesis protocols in addition to purchasing commercially-available kits. In this protocol, we describe the general procedure for how the manual synthesis of a new PET tracer can be automated on one of these radiosynthesizers and validated for the production of clinical-grade tracers. As an example, we use the ELIXYS radiosynthesizer, a flexible cassette-based radiochemistry tool that can support both PET tracer development efforts, as well as routine clinical probe manufacturing on the same system, to produce [18F]Clofarabine ([18F]CFA), a PET tracer to measure in vivo deoxycytidine kinase (dCK) enzyme activity. Translating a manual synthesis involves breaking down the synthetic protocol into basic radiochemistry processes that are then translated into intuitive chemistry "unit operations" supported by the synthesizer software. These operations can then rapidly be converted into an automated synthesis program by assembling them using the drag-and-drop interface. After basic testing, the synthesis and purification procedure may require optimization to achieve the desired yield and purity. Once the desired performance is achieved, a validation of the synthesis is carried out to determine its suitability for the production of the radiotracer for clinical use

Physical and Antibacterial Properties of Peppermint Essential Oil Loaded Poly (ε-caprolactone) (PCL) Electrospun Fiber Mats for Wound Healing

The aim of this study was to fabricate and characterize various concentrations of peppermint essential oil (PEP) loaded on poly(ε-caprolactone) (PCL) electrospun fiber mats for healing applications, where PEP was intended to impart antibacterial activity to the fibers. SEM images illustrated that the morphology of all electrospun fiber mats was smooth, uniform, and bead-free. The average fiber diameter was reduced by the addition of PEP from 1.6 ± 0.1 to 1.0 ± 0.2 μm. Functional groups of the fibers were determined by Raman spectroscopy. Gas chromatography-mass spectroscopy (GC-MS) analysis demonstrated the actual PEP content in the samples. In vitro degradation was determined by measuring weight loss and their morphology change, showing that the electrospun fibers slightly degraded by the addition of PEP. The wettability of PCL and PEP loaded electrospun fiber mats was measured by determining contact angle and it was shown that wettability increased with the incorporation of PEP. The antimicrobial activity results revealed that PEP loaded PCL electrospun fiber mats exhibited inhibition against Staphylococcus aureus (gram-positive) and Escherichia coli (gram-negative) bacteria. In addition, an in-vitro cell viability assay using normal human dermal fibroblast (NHDF) cells revealed improved cell viability on PCL, PCLPEP1.5, PCLPEP3, and PCLGEL6 electrospun fiber mats compared to the control (CNT) after 48 h cell culture. Our findings showed for the first time PEP loaded PCL electrospun fiber mats with antibiotic-free antibacterial activity as promising candidates for wound healing applications

Multipoint-to-point data aggregation using a single receiver and frequency-multiplexed intensity-modulated ONUs

We demonstrate 2.5-GHz-spaced frequency multiplexing capable of aggregating 64 intensity-modulated end-users using low-speed electronic and optoelectronic components. All optical network units (ONUs) achieved high per-user capacity with dedicated optical bands, enabling future low latency applications

49 Gbit/s Direct-Modulation and Direct-Detection Transmission over 80 km SMF-28 without Optical Amplification or Filtering

We demonstrate direct-modulation of a discrete mode laser using Discrete Multi-Tone modulation for transmission distances up to 100 km in the 1550 nm band. A large operational temperature range (0-65ºC) is also demonstrated

Using a newly developed long-period grating filter to improve the timing tolerance of a 320 Gb/s demultiplexer

A 0.8 ps flat top pulse is generated using a long-period fibre grating and used as control pulse for the first time in a 320 Gb/s demultiplexer. The effect is an increased error-free timing tolerance.</p

Artificial Neural Network Based Analysis of High Throughput Screening Data for Improved Prediction of Active Compounds

Artificial Neural Networks (ANNs) are trained using High Throughput Screening (HTS) data to recover active compounds from a large data set. Improved classification performance was obtained on combining predictions made by multiple ANNs. The HTS data, acquired from a Methionine Aminopeptidases Inhibition study, consisted of a library of 43,347 compounds, and the ratio of active to non-active compounds, RA/N, was 0.0321. Back-propagation ANNs were trained and validated using Principal Components derived from the physico-chemical features of the compounds. On selecting the training parameters carefully, an ANN recovers one-third of all active compounds from the validation set with a three-fold gain in RA/N value. Further gains in RA/N values were obtained upon combining the predictions made by a number of ANNs. The generalization property of the back-propagation ANNs was utilized to train those ANNs with the same training samples, after being initialized with different sets of random weights. As a result, only 10% of all available compounds were needed for training and validation, and the rest of the data set was screened with more than a ten-fold gain of the original RA/N value. Thus, ANNs trained with limited HTS data might become useful in recovering active compounds from large data sets