Morphology-Dependent Room-Temperature Ferromagnetism in Undoped ZnO Nanostructures

Since Dietl et al. predicted that Co-doped ZnO may show room-temperature ferromagnetism (RTFM) in 2000, researchers have focused on the investigation of ferromagnetic ZnO doped with various transition metals. However, after decades of exploration, it has been found that undoped ZnO nanostructures can also show RTFM, which in general is dependent on ZnO morphologies. Here, we will give an overall review on undoped ZnO nanomaterials with RTFM. The advanced strategies to achieve multidimensional (quasi-0D, 1D, 2D, and 3D) ferromagnetic ZnO nanostructures and the mechanisms behind RTFM are systematically presented. We have successfully prepared ferromagnetic nanostructures, including thin films, horizontal arrays and vertical arrays. The existing challenges, including open questions about quantum-bound ZnO nanostructures, are then discussed.

An adaptable technique for comparative image assessment: Application to crosswell electromagnetic survey design for fluid monitoring

Reservoir integrity stewardship accompanying carbon capture and sequestration considers fluid extraction and reinjection as a risk-mitigating method against overpressuring that could lead to caprock damage and ensuing [Formula: see text] leakage. Crosswell electromagnetics offers a technically viable monitoring method with the spatial volume coverage necessary for reservoir-encompassing pressure management. However, a certain logistic dilemma for deep gas sequestration into saline and thus electrically conductive aquifers is that crosswell magnetic-field measurements underperform in the imaging of more resistive plume bodies, further exacerbated when vertical arrays intersect, as opposed to surround, plumes. Comparative synthetic-data plume imaging of such scenarios rates the information content of magnetic-field versus electric-field 3D crosswell layouts for reservoir and infrastructure conditions of a representative pilot site in a coastal area in Florida. The image quality of the resulting plume replications can be ranked numerically through a newly proposed semblance qualifier, appraising the model goodness of fit to a given reference. In contrast to common least-squares measures for goodness of fit, the semblance formulation uses classifying logistic function types, thus enabling a better distinction of predefined anomaly features.

A Tolerant Detection Method for Estimating Robust Signal Subspace with Horizontal Arrays in Uncertain Shallow Ocean Environment

The environmental parameters are usually uncertain in complex shallow ocean environment and restrict the performance of the matching model-like method. Therefore, we need a more tolerant detection method for detecting underwater targets in the uncertain shallow ocean environment. The previous mode-subspace detection method has the characteristics of both high performance and robustness. However, the robust mode-subspace detector is suitable for vertical arrays and its performance is limited by shallow ocean environment. Therefore, we propose the tolerant detection method for estimating the robust signal subspace with horizontal arrays. We estimate the robust signal subspace by bringing uncertain parameters into the observation matrix of a horizontal array. Combined with the robust signal subspace estimation, we propose a subspace detector that tolerates uncertain parameters. The results on simulation in a uncertain shallow ocean environment show that the detector we proposed has a high average detection capability and a certain tolerance for uncertain parameters.

Surface seismics with DAS: An emerging alternative to modern point-sensor acquisition

Land seismic acquisition is moving toward “light and dense” geometries, with point receiver systems believed to be an ultimate configuration of choice. Cableless land nodal systems enable more flexible spatial sampling at the price of eliminating even small arrays. For large surveys in a desert environment, such spacing remains insufficient to address the complex near surface, while recordings with single sensors exhibit a significant reduction in data quality. At the same time, exploration problems increasingly demand smaller uncertainty in all seismic products. While 1 m geophone sampling could have addressed these problems, it remains out of economic reach as point sensor cost plateaus. We examine an emerging alternative technology of distributed acoustic sensing (DAS) that revolutionized borehole geophysics but is still mostly unknown in the seismic world. Fully broadband DAS sensors promise massive channel count and uncompromised inline sampling down to 0.25 m. Their distributed nature offers the unique capability to conduct a continuous recording with multiscale grids of “shallow,” “deep,” and “full-waveform inversion” receivers, all implemented with a single set of fixed cables and only one round of shooting. These distinct features allow us to simultaneously pursue near-surface characterization, imaging of deeper targets, and velocity model evaluation. Specifically, in a desert environment, distributed sensors may offer superior data quality compared to point sensors, whereas DAS capability of “seismic zoom” in the near surface becomes instrumental for near-surface characterization. Finally, simultaneous acquisition of surface seismic and vertical arrays that can be achieved easily with DAS can effectively address the exploration of subtler targets such as low-relief structures. We support these findings with a field case study from a desert environment and synthetic examples. With many distinct advantages, surface seismic with DAS emerges as a compelling alternative to modern point-sensor acquisitions.

A recommendation for suitable technologies for an indoor farming framework

Facing food insecurity and overuse of resources due to effects of climate change, humanity needs to find new ways to secure food production and produce close to consumers. Vertical farming, where plants are grown in vertical arrays inside buildings with help of Information and Communication Technology (ICT) components, could contribute to solving this issue. Such systems integrate heterogeneous devices on different computing layers and acquire a lot of data to monitor and optimize the production process. We created an indoor testing unit in which growing conditions can be monitored and controlled to optimize growth of microgreens. This setup includes an Indoor Farming Support as a Service (IFSaaS) prototype that provides safe and secure monitoring and controlling, as well as self-adaption of an indoor farming system. In this article we provide information about the combination of most suitable technologies.