Spatio-Temporal Field Estimation Using Kriged Kalman Filter (KKF) with Sparsity-Enforcing Sensor Placement.

We propose a sensor placement method for spatio-temporal field estimation based on a kriged Kalman filter (KKF) using a network of static or mobile sensors. The developed framework dynamically designs the optimal constellation to place the sensors. We combine the estimation error (for the stationary as well as non-stationary component of the field) minimization problem with a sparsity-enforcing penalty to design the optimal sensor constellation in an economic manner. The developed sensor placement method can be directly used for a general class of covariance matrices (ill-conditioned or well-conditioned) modelling the spatial variability of the stationary component of the field, which acts as a correlated observation noise, while estimating the non-stationary component of the field. Finally, a KKF estimator is used to estimate the field using the measurements from the selected sensing locations. Numerical results are provided to exhibit the feasibility of the proposed dynamic sensor placement followed by the KKF estimation method.

Integrated Circular Economy Model System for Direct Lithium Extraction: From Minerals to Batteries Utilizing Aluminum Hydroxide.

Aluminum hydroxide, an abundant mineral found in nature, exists in four polymorphs: gibbsite, bayerite, nordstrandite, and doyleite. Among these polymorphs gibbsite, bayerite, and commercially synthesized amorphous aluminum hydroxide have been investigated as sorbent materials for lithium extraction from sulfate solutions. The amorphous form of Al(OH)3 exhibits a reactivity higher than that of the naturally occurring crystalline polymorphs in terms of extracting Li+ ions. This study employed high-temperature oxide melt solution calorimetry to explore the energetics of the sorbent polymorphs. The enthalpic stability order was measured to be gibbsite > bayerite > amorphous Al(OH)3. The least stable form, amorphous Al(OH)3, undergoes a spontaneous reaction with lithium, resulting in the formation of a stable layered double hydroxide phase. Consequently, amorphous Al(OH)3 shows promise as a sorbent material for selectively extracting lithium from clay mineral leachate solutions. This research demonstrates the selective direct extraction of Li+ ions using amorphous aluminum hydroxide through a liquid-solid lithiation reaction, followed by acid-free delithiation and relithiation processes, achieving an extraction efficiency of 86%, and the maximum capacity was 37.86 mg·g-1 in a single step during lithiation. With high selectivity during lithiation and nearly complete recoverability of the sorbent material during delithiation, this method presents a circular economy model. Furthermore, a life cycle analysis was conducted to illustrate the environmental advantages of replacing the conventional soda ash-based precipitation process with this method, along with a simple operational cost analysis to evaluate reagent and fuel expenses.

Domain-Informed Neural Networks for Interaction Localization Within Astroparticle Experiments.

This work proposes a domain-informed neural network architecture for experimental particle physics, using particle interaction localization with the time-projection chamber (TPC) technology for dark matter research as an example application. A key feature of the signals generated within the TPC is that they allow localization of particle interactions through a process called reconstruction (i.e., inverse-problem regression). While multilayer perceptrons (MLPs) have emerged as a leading contender for reconstruction in TPCs, such a black-box approach does not reflect prior knowledge of the underlying scientific processes. This paper looks anew at neural network-based interaction localization and encodes prior detector knowledge, in terms of both signal characteristics and detector geometry, into the feature encoding and the output layers of a multilayer (deep) neural network. The resulting neural network, termed Domain-informed Neural Network (DiNN), limits the receptive fields of the neurons in the initial feature encoding layers in order to account for the spatially localized nature of the signals produced within the TPC. This aspect of the DiNN, which has similarities with the emerging area of graph neural networks in that the neurons in the initial layers only connect to a handful of neurons in their succeeding layer, significantly reduces the number of parameters in the network in comparison to an MLP. In addition, in order to account for the detector geometry, the output layers of the network are modified using two geometric transformations to ensure the DiNN produces localizations within the interior of the detector. The end result is a neural network architecture that has 60% fewer parameters than an MLP, but that still achieves similar localization performance and provides a path to future architectural developments with improved performance because of their ability to encode additional domain knowledge into the architecture.