Compact Magnetic Shielding Using Thick-Film Electroplated Permalloy

Compact integration of clocks and inertial sensors using atomic, molecular, and optical (AMO) technology is necessary to create a self-contained navigation system resistant to external interference. However, the trend in miniaturization of AMO systems places the magnetic field of particle traps, optical isolators, and vacuum pumps close to other system components. Stray fields and field fluctuations cause changes in atomic transition frequencies, raising the noise floor and reducing the valuable stability in these precision devices. Therefore, it is critical to shield these magnetic fields away from sensitive subsystems by shunting them through low reluctance paths. This is accomplished with high permeability magnetic materials which either surround the precision components or the source of the magnetic field itself. Current magnetic shields are conventionally machined single or multi-layer structures made of various iron alloys. At smaller size scales, these manufacturing methods are ineffective at accommodating the various device and interconnect shapes, making multi-system integration challenging.This work demonstrates batch fabricated high permeability magnetic shielding using permalloy electroplating techniques to simultaneously push the limits of minimum size, maximum shielding factor, and minimum cost. In particular, it presents the first experimental demonstration of electrodeposited high permeability, compact magnetic shielding at millimeter and sub-millimeter scales of fields exceeding 15 mT. Single layer shields of 300 μm permalloy with inner dimensions varying from 3 mm to 6.5 mm were fabricated on 3D printed polymer molds using a novel double-anode plating process to enable conformal deposition with uniform material properties. Multilayer shields of 10 μm permalloy and copper layers with inner dimensions of 1.5 mm to 6 mm were microfabricated using a bulk micromachining technique. The electroplated shields were designed with appropriate thickness to avoid saturation at the specified fields and with shapes to allow sophisticated interconnect extraction – a task that is challenging for conventional machining yet simple for microfabrication and electroplating. The size and shielding factor of these structures can enable compact integration of magnetic devices for AMO microsystems and other magnetic microelectronics, such as magnetic random-access memory and haptic actuators

Is the GSI anomaly due to neutrino oscillations? - A real time perspective -

We study a model for the "GSI anomaly" in which we obtain the time evolution of the population of parent and daughter particles directly in real time, considering explicitly the quantum entanglement between the daughter particle and neutrino mass eigenstates in the two-body decay. We confirm that the decay rate of the parent particle and the growth rate of the daughter particle do \emph{not} feature a time modulation from interference of neutrino mass eigenstates. The lack of interference is a consequence of the orthogonality of the mass eigenstates. This result also follows from the density matrix obtained by tracing out the unobserved neutrino states. We confirm this result by providing a complementary explanation based on Cutkosky rules applied to the Feynman diagram that describes the self-energy of the parent particle.Comment: 11 page

(3+2)-Cycloaddition Reactions of Oxyallyl Cations

The (3+2)-cycloaddition reaction involving oxyallyl cations has proven to be a versatile and efficient approach for the construction of five-membered carbo- and heterocycles, which are prevalent frameworks in natural products and pharmaceuticals. The following article will provide a brief summary of recent disclosures on this process featuring chemo-, regio- and diastereoselective oxyallyl cycloadditions with both electron-rich and electron-deficient 2π partners

Environmentally conscious approaches to producing viable electrode materials for use in future lithium- and sodium-ion batteries

As technology continues to advance, so does the reliance on portable energy storage. The continued uptake of portable electronic devices, and now the global push for increased electric vehicle uptake, puts heavy demand on the production of batteries to meet these requirements. The work presented in this thesis highlights alternative, environmentally conscious approaches to synthesising and sourcing electrode materials for use in lithium- and sodium-ion batteries. Studies were primarily focused on the structural and electrochemical characterisation of these materials to understand and optimise their performance. The first part of this thesis focuses on studies of a mixed metal compound, which is a product of recycled commercially available batteries. Physical, structural, and electrochemical studies have been carried out to assess the material’s performance and viability as a renewed electrode material for use in ‘second-life’ lithium-ion batteries. The second part of this thesis focuses on hard carbon anodes for sodium-ion batteries. These hard carbons are synthesised via electrodeposition from a ternary eutectic of molten carbonates. Their structures are assessed, along with their electrochemical performance and variability. The work presented in these chapters are envisioned to be an environmentally conscious approach to synthesising new anode materials for batteries

Unsupervised Chunking with Hierarchical RNN

In Natural Language Processing (NLP), predicting linguistic structures, such as parsing and chunking, has mostly relied on manual annotations of syntactic structures. This paper introduces an unsupervised approach to chunking, a syntactic task that involves grouping words in a non-hierarchical manner. We present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to model word-to-chunk and chunk-to-sentence compositions. Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks. Experiments on the CoNLL-2000 dataset reveal a notable improvement over existing unsupervised methods, enhancing phrase F1 score by up to 6 percentage points. Further, finetuning with downstream tasks results in an additional performance improvement. Interestingly, we observe that the emergence of the chunking structure is transient during the neural model's downstream-task training. This study contributes to the advancement of unsupervised syntactic structure discovery and opens avenues for further research in linguistic theory