Matthew Christian Nielsen

Characterization of a preliminary narrow-band absorption material for holographic data storage

A new holographic data storage material is currently being developed at General Electric. This material is based on a thermoplastic host doped with dyes with narrowband absorption resonances. The dye is photosensitive and irreversibly bleaches under exposure to light. The reduction in concentration of the dye in the host after exposure induces refractive index variations at wavelengths away from the dye absorption peak according to the well-known Kramers-Kronig relationship. The approach proposed here requires different recording and readout wavelengths to prevent data erasure during readout. Samples were produced using different dyes and various concentrations in a polycarbonate host. The extensive experience developed in the past with polycarbonate for optical media promises to make the proposed material very inexpensive. These samples were characterized using holographic techniques as well as using spectroscopic transmission and ellipsometry. The results of the characterizations indicate that this material approach can offer very large dynamic range, but have thus far shown very low sensitivity. Further work is required to demonstrate materials having both sufficient dynamic range and sensitivity for holographic storage applications. We believe that if this condition can be satisfied, this new material approach will be an excellent candidate for high-density holographic media for content distribution.

Cost-Optimal Consumption-Aware Electric Water Heating Via Thermal Storage Under Time-of-Use Pricing

Time-of-use electricity pricing is touted as one of the solutions toward optimal load distribution in the future of smart grid. However, the benefits of such an approach are limited as human electricity usage can often be described as inelastic in nature, or not sensitive to pricing in economic theory. Water heating constitutes one of the largest components of such usage. This paper proposes technology to enable residential water heating to be converted from an inelastic to an elastic demand, which would respond to pricing incentives. This is accomplished by utilizing consumer hot water use patterns combined with thermal storage to calculate a user-specific temperature profile. Results demonstrated that such an approach could not only reduce electrical water heating costs significantly and allow for realization of large-scale grid benefits, but also improve customer experience as thermal storage capability would enable more hot water to be delivered at times of peak consumption.

Cost-optimal, robust charging of electrically-fueled commercial vehicle fleets via machine learning

Electrification for commercial vehicle fleets presents opportunity to cut emissions, reduce fuel costs, and improve operational metrics. However, infrastructure limitations in urban areas often inhibit the ability to charge a significant number of electric vehicles, especially under one roof. This paper highlights a novel controls approach developed at GE Global Research in conjunction with Columbia University to fulfill the stated needs for intelligent charging of a commercial fleet of electric vehicles. This novel approach combines traditional control techniques with machine learning algorithms to adapt to customer behavior over time. The stated controls system is designed to regulate the charging rate of multiple electric vehicle supply equipment devices (EVSEs) to facilitate cost-optimal charging subject to past and predicted building load, vehicle energy requirements, and current conditions. In this embodiment, the system is primarily designed to mitigate electric demand charges that may otherwise occur due to charging at inopportune times. The system will be deployed at a New York City FedEx Express delivery depot in partnership with the local utility, Consolidated Edison Company of New York.

The case for open-source PEV charge management data framework

The future growth of electrified transportation could be enhanced through interaction with smart grid system architectures. This paper summarizes current literature regarding plug-in electric vehicle - renewables integration and highlights areas of need to implement such integration at a multi-utility scale. Future smart grid implementations hold the key to transferring information and control parameters between distributed and centralized decision agents. The discussion highlights the opportunity to leverage federal investments in grid renovation and electric vehicle introduction to create system-wide value from improved reliability and reduced fossil fuel consumption. An approach to scaling a previously defined “green signal” methodology for electric vehicles will be detailed across a utility grid balancing region. The research will include a synopsis of the communications opportunities and challenges of coordinating the responses of many resources. The results are expected to show that, with some variation in the charge profiles of individual vehicles, the aggregate set can interface with grid management methods to significantly influence the ability to integrate distributed and centralized renewables to improve environmental quality.

Statistical analyses of two-dimensional photonic bandgap waveguides containing structural deviations

Feasibility studies and statistical analyses generated using specially tailored software tools of 2D photonic bandgap waveguides containing structural deviations are presented. Waveguide structures comprising two wide angle bends within a photonic bandgap structure were used in this analysis. The results demonstrate that small variations in the location and size of ordered media create significant changes in the photonic bandgap properties of the material. Deviations with a statistical mean of 20 nm in locaiton and size of this particular model designed for 1.55 μm wavelength are enough to cause the waveguide transmission to reduce by up to 90%.

Cloud connected smart grid enabled EVSE

Electric vehicles are getting lot of attention during the last few years around the world to deal with environmental concerns as well as to increase economic stability by reducing oil consumption. But this new technology can pose a threat to the utilities by adding extra demand on an electric grid. In this paper, we propose a methodology to interface the utility with electric vehicle supply equipment (EVSE). This system offers two-way real-time information and communication between EVSEs and utility through a cloud server. Using this technique, a utility would be more tightly coupled with the load being served providing benefit to both the utility and consumer such as advanced protection schemes and downward pressure on cost.

Thermoplastic media for holographic data storage

The growing prevalence of digital technologies has led to increased data generation so that new storage technologies must be developed to handle expanding capacity demand. Holographic data storage is a very promising candidate with the potential to provide ultra-high density data storage. Currently, many teams are developing holographic storage technology, with much of the emphasis on professional archival applications. However, consumer-oriented applications are also growing rapidly and the requirements for these applications are different from those for professional archival storage. In particular, a holographic medium for consumer applications must be simple, cheap, and easy to process. In addition, where content distribution is the intended application, the medium must also be compatible with mastering and replication processes. We present a new holographic medium designed to meet the requirements of consumer oriented applications. The media is based on thermoplastic materials that are modified by the inclusion of photo-chemically active dyes. A series of 0.6 and 1.2 mm thick discs were injection molded and characterized for holographic storage capacity and sensitivity. The first series of samples showed large refractive index modulations of 0.03 but a poor sensitivity of 0.1 cm/J. Analysis of the data showed that the low sensitivity limited the usable capacity of the media to M/# values of ~1. A new series of dyes were synthesized with optimized efficiency and injection molded in 1.2 mm substrates. These substrates demonstrated comparable usable capacity but with significantly increased sensitivities. The results of the measurements of the injection-molded thermoplastic media are presented.

Toward multifunctional optical integration: an adaptive lithographic method for patterning optical structures

A new method of interconnecting various optoelectronic components is discussed. Offset error up to 25 microns can be corrected to achieve single mode alignment accuracies. Several planar optical devices were photocomposed using the adaptive photolithographic method and these have been shown to perform with the desired characteristics.

Improved Sensitivity of Dye-Doped Thermoplastic Discs for Holographic Data Storage

Significant sensitivity improvement was achieved in dye-doped thermoplastic materials for holographic data storage at 405nm. The sensitivity characterization method and measurement results are reported.

Radiation mode coupling between active and passive chips based on a self-formed compact polymer interconnect for single-mode chip-to-chip optoelectronic integration

Based on radiation mode coupling through a self-formed polymer waveguide extension, efficient single-mode optical coupling can be achieved between active and passive chips while relaxing the stringent positioning requirements. A 20dB improvement can be achieved according to simulation results. Single-mode waveguides have been successfully demonstrated using GE photo-definable polymer materials.