Quantum computers represent data through qubits, as opposed to bits in classical computers. These qubits can be implemented using various physical systems, including trapped ions where they are represented by the internal energy levels of individual ions confined within electromagnetic fields. Trapped ions are an attractive choice for qubit implementation since this system has the potential to meet all the DiVincenzo criteria, which are a set of requirements needed to build a mainstream quantum computer. To facilitate the development of mega-qubit (MQb) trapped-ion quantum technologies, the Scalable Quantum Research Lab is conducting extensive research on the persistent issue of collisions with background gas molecules, an error immune to the standard quantum error correcting codes. My research focused on answering the question, 'Can error rates be controlled through trap design?'. To answer this question, there are 3 parameters to determine: (1) trap height: vertical location of the ions from the surface trap; (2) trap depth: how strong the trap is (i.e., how stable is the trapping potential); and (3) trap anharmonicities: the coefficients associated with polynomial potential. These anharmonic potentials can accelerate ions after collisions, thereby increasing collision errors. These results were found using Particle-in-Cells simulations and computational analysis for error minimization. Optimizing the trap design allowed greater control over the collision error rate for a long ion-chain trap. In short, finding a way to control anharmonicity and trap depth using trap geometric optimization can reduce the additional measured errors in the bigger experiments. The results presented are a model of trapped ion and graphs showing relationship between different trap paramters and the three variables mentioned above. 

With recent advances in wearable technologies, there is a growing demand for high power density batteries with more complex geometries. However, conventional battery manufacturing processes such as blade casting are incapable of producing the desired complex form factors. As an alternative manufacturing method, researchers are using additive manufacturing (AM), which allows for the rapid and efficient production of complexly shaped lithium-ion batteries (LIBs). A commonly used type of AM is direct-ink write (DIW) printing, a manufacturing technique where material is extruded through a syringe using displacement-controlled mechanisms. However, DIW typically lags from when pressure is applied to the syringe to when the battery material gets dispensed onto the surface. This lag can result in the printing of inaccurate features that negatively impact battery performance or even cause device failure. To account for this lag, we created a software module using the programming language C#, allowing users to print with micron-level precision. The module was integrated into the Rhino and Grasshopper platforms, a commercial computer-aided design (CAD) software package, enabling direct application into CAD models. The module can accept a list of curves as an input and will output a transformed list of curves that are more accurate to the CAD design. This module eases the challenge of printing material at the micron level, however, further research must be conducted to implement this module into lithium-ion batteries AM.

The purpose of this experiment was to visualize and record the different rates of expansion for multiple gases as they are launched into the higher parts of Earth’s atmosphere with a High-Altitude Balloon (HAB). The ideal gas law models the behavior of a gas that of which its molecules occupy no volume and have no intermolecular forces (IMF). It is a simple equation; however, it cannot model gases accurately. On the other hand, Van der Waals equation for non-ideal gases better resembles the behavior of a real gas as it includes what the ideal gas law lacks. To test this, we filled three syringes with three different gases to the same volume. We chose to test argon, helium, and nitrogen. We secured the syringes to a container, which served as the payload for the HAB. We also placed an altimeter, thermometer, and a barometric pressure sensor inside the container. Then, we connected the sensors to an Arduino to record each piece of data synced to a stopwatch that is displayed in the container on a screen. Finally, we secured a camera to the container facing the stopwatch and syringes to record the gasses’ volume. Because helium has the weakest IMFs out of the three gases, we believed helium would expand at a higher rate as atmospheric pressure decreases compared to the other gases. The results from our experiment serve as a good example of how far the behavior of real gases deviate from ideal gases modeled by the ideal gas law. Depending on how close our measured values reach the calculated values from the ideal gas law, we can predict which situations the ideal gas law can model the behavior of a particular gas relatively accurately.

The informational industry’s share of energy expenditures is growing, and the power consumed by network communications has grown faster than others. The growing importance of machine learning (ML), artificial intelligence, and big data will only compound this trend. There is a need for easily manufactured low-cost energy-efficient communications to balance the needs of industry stakeholders and the environment. One such avenue is using perovskite LEDs (PeLEDs) to transmit data over short distances via intensity-modulated light. For PeLEDs to meet the required informational throughput, they must be modulated as frequently as possible without compromising their integrity. The inherent non-linear current-voltage characteristics and charge-carrier behavior of PeLEDs invite experimentation into the signals used to modulate devices at high frequencies. With the use of an automated laboratory testing environment, many high-frequency driving waveforms were used to drive PeLEDs for data on PeLED voltage, current, and photoemission to quantify device performance. Generative ML models trained to associate waveforms with frequency, device lifetime, and power efficiency are then used to generate potentially superior waveforms for testing. This process is repeated iteratively until the best driving waveforms are identified, forming a closed-loop optimization cycle. The expected results of this study are an understanding of how driving waveforms affect key PeLED performance metrics, and which driving waveforms ensure higher frequency, efficiency, and longevity in industrial applications.

In an ideal world, the electrical grid could fully decarbonize with just solar and wind as forms of energy generation. However, more traditional and firm forms of generation, such as natural gas or nuclear power, support the stability of the electric grid and lower the cost of transition to a net-zero carbon grid. To properly integrate emerging technologies, such as carbon capture and sequestration (CCS), as sources of firm generation, there needs to be an understanding of their economic behavior, and larger impact on other forms of electrical generation. CCS is of interest because the natural gas industry supplies the cheapest electricity and plays a major role in planning the energy transition. This project seeks to understand the economic viability of CCS to inform policy encouraging the deployment of emerging electricity resources. How do the investment costs associated with CCS need to change to result in significant buildout of natural gas plants with CCS? How does the increase of CCS buildout impact other forms of generation within a given system, and contribute to the overarching goal of creating a sustainable energy future? Using the MIT Energy Initiative's capacity-expansion model GenX, the investment costs associated with adding new natural gas plants with CCS and with retrofitting existing plants with CCS are varied in a sweep and the impact on the amount of added capacity of CCS and other forms of generation is analyzed. It has been found that an 80% reduction of the investment costs associated with CCS begins to promote the buildout of new CCS plants. Further investigation on the impact to other power sources, particularly battery storage, will provide insight into the impact of expanding CCS capacity on the rest of the system, with the anticipated result that increasing CCS buildout discourages the buildout of already established power sources.
 

Global Positioning Systems (GPS) technology plays a pivotal role in ensuring the safe and efficient navigation of drones by providing near real-time tracking of location and speed. The precision and reliability of GPS receivers are crucial for effective planning, sensing, and control applications in various domains. As Unmanned Aerial Vehicles (UAVs) continue to rise in demand and predominantly rely on GPS, minimizing the uncertainty in GPS performance becomes imperative. UAVs utilize the cost-effective nature of Micro-electromechanical Systems (MEMS) GPS receivers. This study identifies and analyzes GPS errors, specifically within consumer-grade MEMS receivers. The MEMS receivers are preferred for their low cost, low power, and low weight, making them ideal for integration into UAVs. Our methods include a series of controlled experiments in urban and semi-urban environments, encompassing varying weather conditions such as sunny and cloudy days. Static experiments evaluate GPS signal accuracy under stationary conditions, while dynamic experiments monitor GPS performance during drone flights. Our preliminary findings have shown a range of inaccuracies in GPS signal measurements. Horizontal signal accuracy varied from +/-1 to +/-14 meters, while vertical signal accuracy ranged from +/-3 to +/-12 meters. These results underscore the significance of further investigation to enhance GPS reliability, particularly in scenarios critical for UAV operations. In ongoing research, we are conducting more testing in other geographical locations and weather conditions to ensure the robustness of our conclusion. Additionally, we are developing environment-specific error detection algorithms utilizing the sensor fusion approach. Merging data from multiple sensors can reduce the uncertainty of an object's location, helping us when the GPS technology is not fully reliable. Our research contributes to advancing GPS technology capabilities, particularly for UAVs where accurate localization is important.

In quantum computing, computer engineers require a method to control the logical state of a quantum bit (qubit). Unlike its classical counterpart, a qubit’s logical state is not defined by a binary and discrete voltage. The QT3 lab is developing a quantum testbed with a defect in diamond known as the nitrogen-vacancy (NV) center. We present an on-diamond antenna that is optimized to manipulate the electron spin state of an NV center, which defines the qubit. Applying a radiofrequency magnetic field equal to the energy difference between the two spin states of our qubit, also known as resonant excitation, enables control of this state. The strength of this field directly correlates to the frequency at which this manipulation may occur. That frequency is known as the Rabi frequency. This is important as we want this frequency to be faster than the state can undergo decoherence, where state information is lost to the environment of the qubit. We have designed and simulated an antenna using finite element analysis software, which will supply our field and be fabricated on the diamond surface. For its geometry we realized a coplanar waveguide with a shorted end shaped around the NV center, which optimizes the field strength at the NV center, power reflections, and area consumption. Preliminary fabricated samples have been mounted, wirebonded, and characterized using a vector network analyzer, and have shown behavior that aligns with simulated results. We expect to have the antenna fabricated on our single NV center testbed sample and achieve a Rabi frequency on the order of 10’s of MHz. Once this sample is fully integrated into our cryogenic system, it will enable us to expand control to multiple nuclear spin qubits from a single NV center, as a quantum register. The testbed will be accessible to researchers and educators.