DESIGN OF DEEP-FERTILIZATION MECHANISM WITH DEFORMED GEARS AND PERFORMANCE TESTS

Deep-fertilization mechanism is a key part of deep-fertilization liquid fertilizer applicator. To obtain a good-performance deep-fertilization mechanism, this study developed a deep-fertilization mechanism with deformed gears and designed a deformed gear fertilization test bench. Single-factor and central composite design tests were performed with the planet carrier, spray hole size and pump pressure as the test factors, and the fertilizer amount as the test index. The results of the single-factor test showed a linear functional relationship between fertilizer amount and pump pressure, an exponential functional relationship between planet carrier velocity and fertilizer amount, and an exponential relationship between spray hole size and fertilizer amount. The rotating and perpendicular test data were analyzed and optimized using Design-Expert 8.0.5 software. The result of the optimization is: 10.5ml of fertilizer amount with pump pressure 0.36MPa, planet carrier velocity 82 r/min, and spray hole size 2 mm. The test result can meet the agronomic requirements.

Does carrier velocity saturation help to enhance fmax in graphene field-effect transistors?

A drift–diffusion model including self-heating effects in graphene transistors to investigate carrier velocity saturation for optimal high frequency performance.

A carrier velocity model for electrical detection of gas molecules

Nanomaterial-based sensors with high sensitivity, fast response and recovery time, large detection range, and high chemical stability are in immense demand for the detection of hazardous gas molecules. Graphene nanoribbons (GNRs) which have exceptional electrical, physical, and chemical properties can fulfil all of these requirements. The detection of gas molecules using gas sensors, particularly in medical diagnostics and safety applications, is receiving particularly high demand. GNRs exhibit remarkable changes in their electrical characteristics when exposed to different gases through molecular adsorption. In this paper, the adsorption effects of the target gas molecules (CO and NO) on the electrical properties of the armchair graphene nanoribbon (AGNR)-based sensor are analytically modelled. Thus, the energy dispersion relation of AGNR is developed considering the molecular adsorption effect using a tight binding (TB) method. The carrier velocity is calculated based on the density of states (DOS) and carrier concentration (n) to obtain I–V characteristics and to monitor its variation in the presence of the gas molecules. Furthermore, the I–V characteristics and energy band structure of the AGNR sensor are simulated using first principle calculations to investigate the gas adsorption effects on these properties. To ensure the accuracy of the proposed model, the I–V characteristics of the AGNR sensor that are simulated based both on the proposed model and first principles calculations are compared, and an acceptable agreement is achieved.

Strapdown Sculling Velocity Algorithms Using Novel Input Combinations

Sculling motion is a standard input to evaluate the performance of the velocity algorithm in a highly dynamic environment. Conventional sculling algorithms usually adopt incremental angle/specific force increments or angular rate/specific force as algorithm inputs. However modern inertial sensors have different output types now, which do not correspond to the inputs of those traditional algorithms. For example, some inertial sensors have the integrated angular rate (incremental angle)/specific force outputs or angular rate/specific force increments outputs. Hence the conventional sculling algorithms cannot be easily applied to these situations. A novel sculling algorithm using incremental angle/specific force inputs or angular rate/specific force increments inputs is developed in this paper. The advantage of the novel algorithm is that it can calculate the carrier velocity directly without converting the dimension of inertial sensor outputs values. Theoretical analysis, digital simulations, and a trial study are carried out to verify our algorithm. The results demonstrate that for corresponding types of strapdown inertial navigation systems (SINS) the novel sculling algorithm exhibits better performance than the conventional sculling velocity algorithms.