Fabrication and Characterization of Dye-Sensitized Solar Cells Using Bixa orellana Seeds and Basella alba Leaves
Dye-sensitized solar cells (DSSCs) have cheaper and easier means of fabrication compared to the currently used solar cells, which are mostly silicon-based, so DSSCs are developed for a prospect of solar energy accounting for a higher percentage in the world’s total energy production, which is currently 0.1%. However, compared to their inorganic counterparts, their efficiencies are low, and the search for a dye that will maximize the potential of DSSCs is still ongoing. The aim of this study is to be able to evaluate the absorption range in the solar spectrum of dyes extracted from Basella alba leaves and Bixin orellana seeds, and of dyes resulting from the mixture of both extracts, using UV-Vis Spectrophotometer, with the objective of increasing the absorption; to be able to fabricate functional DSSCs from the individual and mixed dyes; and to be able to evaluate the different conversion efficiencies of the DSSCs of the individual and mixed dyes using Linear Sweep Voltammetry, with the aim of increasing the conversion efficiency due to a wider absorption range. B. alba leaves and B. orellana seeds were extracted using soxhlet extraction. The clean extracts were mixed in different proportions, and were characterized using UV-Vis Spectrophotometer. The two individual dyes together with two proportions of the mixed B. alba:B. orellana dyes, 1:1 and 2:1, were then incorporated into DSSCs. In the fabrication of DSSCs, twelve plates of Fluorine doped tin oxide were coated with titanium dioxide (TiO2) using spray pyrolysis. They were sintered and scraped, and were afterwards immersed in the four dyes for four days. Platinum plates were placed on top, and iodine-triiodide couple electrolyte was introduced via capillary action. The sealed DSSCs were subjected to Linear Sweep Voltammetry under dark and illuminated conditions, using a sun simulator. Results from the UV-Vis spectrophotometry showed that mixing the dyes had increased the absorption range of the individual dyes, although not superpositionally, and that the 2:1 mixed dye has the most potential. Being incorporated into DSSCs, the dyes, including the mixed ones, have successfully converted solar energy into electrical energy, as shown by the significance in conversion efficiencies under dark and illuminated conditions. However, despite the increase in the absorption range, neither of the mixed dyes have shown a higher conversion efficiency than the individual ones, which can be accounted for a possible weaker interaction between the two dyes and the TiO2, resulting to lower efficiencies. The study has been able to obtain and characterize dyes from B. orellana seeds and B. alba leaves and has been able to incorporate the dyes into DSSCs. With the wider absorption range of the mixed dyes, the study has been able to confirm the possibility of the dyes to maximize the potential of DSSCs, as shown by the successful conversion of solar energy into electrical energy of all fabricated DSSCs, including those of mixed dyes. If the possible problem with the dye-dye as well as the dye-TiO2 interactions could be solved, the possibility of much higher conversion efficiencies could be expected.
Finding a Better Brain Booster
The purpose of my project was to determine which activity improved academic performance the most: 10 minutes of exercise, 10 minutes of Brain Gym (cross-lateral movements), or a combination of 5 minutes exercise and 5 minutes Brain Gym. This project was conducted to find which activity would be a better learning aid in grade 4 students. There were many steps to conducting this study. First, I located teachers and classes, willing to allow the testing and determined days and times to test. Next, I created consent forms to explain the project and had permission forms from the students and their parents signed. Then I prepared 15 math and reading tests at students’ grade level. There were two tests for 15 days- labeled Before Test and After Test. When I administered the tests for the classes doing the activities, I made sure the tests were given by the classroom teacher, which kept the students motivated. Tests were given every day for 5 days. The Before Test was given at 1:00 pm. Then at 1:30 pm students did 10 minutes of the planned activity for that week. Immediately after the activity, students were given the After Test. This was done every day and was repeated for Exercise, Brain Gym, and Combination week. The control class was designed to determine if doing the test for a second time in a day improved the student’s performance. The Before Test was given at 1:00 pm and then at 1:30 pm. When the Before Tests were completed, the students continued with their daily work for 10 minutes. Then at 1:40 pm students were given the After Test. This was repeated for Exercise, Brain Gym, and Combination week. Students were given 10 minutes to complete the scheduled activity that week; either Exercises, Brain Gym or a combination of the two. During the week of Exercise, Jumping jacks, Skipping, Stride jumps, Burpies, Twisties and Jogging on the spot were completed in the 10 minutes provided. During the Brain Gym week, there were 26 Brain Gym movements. The lists of movements were rotated each day; so all movements were performed. The test results of this study were intriguing. (Bar=Standard Deviation) I concluded that 10 minutes of exercise was the better brain booster. Exercise improved academic performance by 9.8%. Brain Gym improved academic performance by 0.2% and the combination activities increased test scores by an average of 3.2%. The control class test scores decreased on average by 1.0% indicating that writing the tests twice in a day, did not improve students’ test scores. Exercising for 10 minutes improved student’s math scores by 11.0% and reading scores by 6.9%. Brain Gym math test scores decreased by 1.4%, and increased in reading by 1.8%. In the combination of the two, the math test scores decreased by 1.0%, reading test scores increased by 7.4%. Overall, the most effective and reliable brain booster was 10 minutes of mild aerobic exercise alone.
Self driving car
Autonomous car is a very new concept, being a car without any driver. Several concurrent software process data using Artificial Intelligence to recognize and propose a path which the car should follow. The goal of the project is that a driverless car can reduce the distance between the cars, lowering the degree of road loadings, reducing the number of traffic jams, avoid human errors, and allowing people with disabilities(even blind people) to travel using an autonomous car. Theoretically a car without driver in the future should be much safer, because human reaction speed is higher than 200 ms, and the computing power of the newest computers allows traffic calculations even to 10 ms. The necessary power is provided by three multi-core laptops that process with Artificial Intelligence in order to recognize traffic signs, traffic lanes , traffic car fingerprints, processing the data from a 3D radar, using particle filters to localize car in a GPS map, the management of database with traffic signs, magnetic sensors, acceleration sensors, a distributed software, a supervisory system and the software which drives the stepper motor to turn the steering wheel (acceleration and braking). Currently the software is able to recognize the traffic signs, register them in a database using Google Maps. The fields record the sign and direction of travel from that area. Each car participating in the traffic and using this software will register new signs detected and the will modify the degree of confidence of recognition for other users. Another software component is able to recognize the demarcation lines between lanes, with three cameras to calculate exactly or using probabilities where it is on the road, where the roadsides are and to propose a new direction even in the absence of traffic signs for the next seconds. Another part of the software is trying to use Artificial Intelligence to detect other car fingerprints from webcam images. The calculation was performed on 3 computers, requiring distributed processing. I developed a management information system based on semaphores that allows data processing and supervision from 3 different computers. This project presents a hardware version of a LIDAR – a 3D radar and a software for creating a 3D environment in which the car navigates and using it the car will take decision to avoid obstacles. The LIRDAR contains a total of 16 avalanche photo-detector mounted on a stepper motor that spins at a frequency of 10 Hz. The information provided by my radar is about 576.000 pixels at resolution of 10 bits. The 3D radar helps the entire software system to increase the confidence of decision.