Progressive amorphization of GeSbTe phase-change material under electron beam irradiation

Fast and reversible phase transitions in chalcogenide phase-change materials (PCMs), in particular, Ge-Sb-Te compounds, are not only of fundamental interests, but also make PCMs based random access memory (PRAM) a leading candidate for non-volatile memory and neuromorphic computing devices. To RESET the memory cell, crystalline Ge-Sb-Te has to undergo phase transitions firstly to a liquid state and then to an amorphous state, corresponding to an abrupt change in electrical resistance. In this work, we demonstrate a progressive amorphization process in GeSb2Te4 thin films under electron beam irradiation on transmission electron microscope (TEM). Melting is shown to be completely absent by the in situ TEM experiments. The progressive amorphization process resembles closely the cumulative crystallization process that accompanies a continuous change in electrical resistance. Our work suggests that if displacement forces can be implemented properly, it should be possible to emulate symmetric neuronal dynamics by using PCMs

Recovery mechanisms and formation influencing factors of miscible CO2 huff-n-puff processes in shale oil reservoirs: A systematic review

Shale oil production is vital for meeting the rising global energy demand, while primary recovery rates are poor due to the ultralow permeability. CO2 huff-n-puff can boost yields by enabling key enhanced oil recovery mechanisms. This review examines the recent research on mechanisms and formation factors influencing CO2 huff-n-puff performance in shale liquid reservoirs. During the soaking period, oil swelling, viscosity reduction and CO2-oil miscibility occur through molecular diffusion into shale nanopores. The main recovery mechanism during the puff period is depressurization with oil desorption and elastic energy release. The interplay between matrix permeability and fracture network directly determines the CO2 huff-n-puff performance. Nanopore confinement, wettability alterations, and heterogeneity also significantly impact the huff-n-puff processes, with controversial effects under certain conditions. This work provides an integrated discussion on the mechanistic insights and formation considerations essential for the advancement of CO2 huff-n-puff application in shale reservoirs. By synthesizing the recent research findings, we aim to spotlight the key challenges and opportunities in considering reservoirs for this process, thereby contributing to the advancement of CO2 huff-n-puff applications for enhanced oil recovery.Ducument Type: Invite reviewCites as: Wan, Y., Jia, C., Lv, W., Jia, N., Jiang, L., Wang, Y. Recovery mechanisms and formation influencing factors of miscible CO2 huff-n-puff processes in shale oil reservoirs: A systematic review. Advances in Geo-Energy Research, 2024, 11(2): 88-102. https://doi.org/10.46690/ager.2024.02.0

A robust and efficient statistical method for genetic association study using case and control samples from multiple cohorts

BACKGROUND: The theoretical basis of genome-wide association studies (GWAS) is statistical inference of linkage disequilibrium (LD) between any polymorphic marker and a putative disease locus. Most methods widely implemented for such analyses are vulnerable to several key demographic factors and deliver a poor statistical power for detecting genuine associations and also a high false positive rate. Here, we present a likelihood-based statistical approach that accounts properly for non-random nature of case–control samples in regard of genotypic distribution at the loci in populations under study and confers flexibility to test for genetic association in presence of different confounding factors such as population structure, non-randomness of samples etc. RESULTS: We implemented this novel method together with several popular methods in the literature of GWAS, to re-analyze recently published Parkinson’s disease (PD) case–control samples. The real data analysis and computer simulation show that the new method confers not only significantly improved statistical power for detecting the associations but also robustness to the difficulties stemmed from non-randomly sampling and genetic structures when compared to its rivals. In particular, the new method detected 44 significant SNPs within 25 chromosomal regions of size < 1 Mb but only 6 SNPs in two of these regions were previously detected by the trend test based methods. It discovered two SNPs located 1.18 Mb and 0.18 Mb from the PD candidates, FGF20 and PARK8, without invoking false positive risk. CONCLUSIONS: We developed a novel likelihood-based method which provides adequate estimation of LD and other population model parameters by using case and control samples, the ease in integration of these samples from multiple genetically divergent populations and thus confers statistically robust and powerful analyses of GWAS. On basis of simulation studies and analysis of real datasets, we demonstrated significant improvement of the new method over the non-parametric trend test, which is the most popularly implemented in the literature of GWAS

GRAINS: Proximity Sensing of Objects in Granular Materials

Proximity sensing detects an object's presence without contact. However, research has rarely explored proximity sensing in granular materials (GM) due to GM's lack of visual and complex properties. In this paper, we propose a granular-material-embedded autonomous proximity sensing system (GRAINS) based on three granular phenomena (fluidization, jamming, and failure wedge zone). GRAINS can automatically sense buried objects beneath GM in real-time manner (at least ~20 hertz) and perceive them 0.5 ~ 7 centimeters ahead in different granules without the use of vision or touch. We introduce a new spiral trajectory for the probe raking in GM, combining linear and circular motions, inspired by a common granular fluidization technique. Based on the observation of force-raising when granular jamming occurs in the failure wedge zone in front of the probe during its raking, we employ Gaussian process regression to constantly learn and predict the force patterns and detect the force anomaly resulting from granular jamming to identify the proximity sensing of buried objects. Finally, we apply GRAINS to a Bayesian-optimization-algorithm-guided exploration strategy to successfully localize underground objects and outline their distribution using proximity sensing without contact or digging. This work offers a simple yet reliable method with potential for safe operation in building habitation infrastructure on an alien planet without human intervention.Comment: 35 pages, 5 figures,2 tables. Videos available at https://sites.google.com/view/grains2/hom