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Cover page of Correlation-driven electron-hole asymmetry in graphene field effect devices

Correlation-driven electron-hole asymmetry in graphene field effect devices

(2022)

Electron-hole asymmetry is a fundamental property in solids that can determine the nature of quantum phase transitions and the regime of operation for devices. The observation of electron-hole asymmetry in graphene and recently in twisted graphene and moiré heterostructures has spurred interest into whether it stems from single-particle effects or from correlations, which are core to the emergence of intriguing phases in moiré systems. Here, we report an effective way to access electron-hole asymmetry in 2D materials by directly measuring the quasiparticle self-energy in graphene/Boron Nitride field-effect devices. As the chemical potential moves from the hole to the electron-doped side, we see an increased strength of electronic correlations manifested by an increase in the band velocity and inverse quasiparticle lifetime. These results suggest that electronic correlations intrinsically drive the electron-hole asymmetry in graphene and by leveraging this asymmetry can provide alternative avenues to generate exotic phases in twisted moiré heterostructures.

Cover page of Correlation-driven electronic reconstruction in FeTe1−xSex

Correlation-driven electronic reconstruction in FeTe1−xSex

(2022)

Electronic correlation is of fundamental importance to high temperature superconductivity. While the low energy electronic states in cuprates are dominantly affected by correlation effects across the phase diagram, observation of correlation-driven changes in fermiology amongst the iron-based superconductors remains rare. Here we present experimental evidence for a correlation-driven reconstruction of the Fermi surface tuned independently by two orthogonal axes of temperature and Se/Te ratio in the iron chalcogenide family FeTe1−xSex. We demonstrate that this reconstruction is driven by the de-hybridization of a strongly renormalized dxy orbital with the remaining itinerant iron 3d orbitals in the emergence of an orbital-selective Mott phase. Our observations are further supported by our theoretical calculations to be salient spectroscopic signatures of such a non-thermal evolution from a strongly correlated metallic phase into an orbital-selective Mott phase in dxy as Se concentration is reduced.

Cover page of Genotype to ecotype in niche environments: adaptation of Arthrobacter to carbon availability and environmental conditions

Genotype to ecotype in niche environments: adaptation of Arthrobacter to carbon availability and environmental conditions

(2022)

AbstractNiche environmental conditions influence both the structure and function of microbial communities and the cellular function of individual strains. The terrestrial subsurface is a dynamic and diverse environment that exhibits specific biogeochemical conditions associated with depth, resulting in distinct environmental niches. Here, we present the characterization of seven distinct strains belonging to the genus Arthrobacter isolated from varying depths of a single sediment core and associated groundwater from an adjacent well. We characterized genotype and phenotype of each isolate to connect specific cellular functions and metabolisms to ecotype. Arthrobacter isolates from each ecotype demonstrated functional and genomic capacities specific to their biogeochemical conditions of origin, including laboratory-demonstrated characterization of salinity tolerance and optimal pH, and genes for utilization of carbohydrates and other carbon substrates. Analysis of the Arthrobacter pangenome revealed that it is notably open with a volatile accessory genome compared to previous pangenome studies on other genera, suggesting a high potential for adaptability to environmental niches.

Cover page of High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration

(2022)

AbstractWith the goal of accelerating the design and discovery of metal–organic frameworks (MOFs) for electronic, optoelectronic, and energy storage applications, we present a dataset of predicted electronic structure properties for thousands of MOFs carried out using multiple density functional approximations. Compared to more accurate hybrid functionals, we find that the widely used PBE generalized gradient approximation (GGA) functional severely underpredicts MOF band gaps in a largely systematic manner for semi-conductors and insulators without magnetic character. However, an even larger and less predictable disparity in the band gap prediction is present for MOFs with open-shell 3d transition metal cations. With regards to partial atomic charges, we find that different density functional approximations predict similar charges overall, although hybrid functionals tend to shift electron density away from the metal centers and onto the ligand environments compared to the GGA point of reference. Much more significant differences in partial atomic charges are observed when comparing different charge partitioning schemes. We conclude by using the dataset of computed MOF properties to train machine-learning models that can rapidly predict MOF band gaps for all four density functional approximations considered in this work, paving the way for future high-throughput screening studies. To encourage exploration and reuse of the theoretical calculations presented in this work, the curated data is made publicly available via an interactive and user-friendly web application on the Materials Project.

Cover page of Telemedicine implementation and use in community health centers during COVID-19: Clinic personnel and patient perspectives.

Telemedicine implementation and use in community health centers during COVID-19: Clinic personnel and patient perspectives.

(2022)

In March 2020, federal and state telehealth policy changes catalyzed telemedicine adoption and use in community health centers. There is a dearth of evidence on telemedicine implementation and use in these safety net settings and a lack of information reflecting the perspectives of patients with limited English proficiency. We conducted in-depth interviews with clinic personnel and patients during the pandemic in two federally qualified health centers that primarily serve Chinese and Latino immigrants. Twenty-four interviews (clinic personnel ​= ​15; patients who primarily speak a language other than English ​= ​9) were completed remotely between December 2020 and April 2021. Interview scripts included questions about their telemedicine experiences, technology, resources and needs, barriers, facilitators, language access, and continued use, with a brief socio-demographic survey. Data analyses involved a primarily deductive approach and thematic analysis of transcript content. Both FQHCs adopted telemedicine in a few weeks and transitioned primarily to video and audio-only visits within two months. Findings reveal third-party language interpretation services were challenging to integrate into telemedicine video visits. Bilingual personnel who provided language concordant care were seen as essential for efficient and high-quality patient telemedicine experiences. Audio-only visits were of particular benefit to reach patients of older age, with limited English proficiency, and with limited digital literacy. Continued use of telemedicine is contingent on reimbursement policy decisions and interventions to increase patient digital literacy and technological resources. Results highlight the importance of reimbursing audio-only visits post-pandemic and investing in efforts to improve the quality of language services in telemedicine encounters.

Cover page of Approaches for handling high-dimensional cluster expansions of ionic systems

Approaches for handling high-dimensional cluster expansions of ionic systems

(2022)

Disordered multicomponent systems attract great interest due to their engineering design flexibility and subsequent rich space of properties. However, detailed characterization of the structure and atomic correlations remains challenging and hinders full navigation of these complex spaces. A lattice cluster expansion is one tool to obtain configurational and energetic resolution. While in theory a cluster expansion can be applied to any system of any dimensionality, the method has primarily been used in binary systems or ternary alloys. Here we apply cluster expansions in high-component ionic systems, setting up the largest cluster expansion ever attempted to our knowledge. In doing so, we address and discuss challenges specific to high-component ionic systems, namely charge state assignments, structural relaxations, and rank-deficient systems. We introduce practical procedures to make the fitting and analysis of complex systems tractable, providing guidance for future computational studies of disordered ionic systems.

Cover page of Nonsymmorphic symmetry-protected band crossings in a square-net metal PtPb4

Nonsymmorphic symmetry-protected band crossings in a square-net metal PtPb4

(2022)

Topological semimetals with symmetry-protected band crossings have emerged as a rich landscape to explore intriguing electronic phenomena. Nonsymmorphic symmetries in particular have been shown to play an important role in protecting the crossings along a line (rather than a point) in momentum space. Here we report experimental and theoretical evidence for Dirac nodal line crossings along the Brillouin zone boundaries in PtPb4, arising from the nonsymmorphic symmetry of its crystal structure. Interestingly, while the nodal lines would remain gapless in the absence of spin–orbit coupling (SOC), the SOC, in this case, plays a detrimental role to topology by lifting the band degeneracy everywhere except at a set of isolated points. Nevertheless, the nodal line is observed to have a bandwidth much smaller than that found in density functional theory (DFT). Our findings reveal PtPb4 to be a material system with narrow crossings approximately protected by nonsymmorphic crystalline symmetries.

Cover page of A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson's disease: a brain radiomics study.

A multiple-tissue-specific magnetic resonance imaging model for diagnosing Parkinson's disease: a brain radiomics study.

(2022)

Brain radiomics can reflect the characteristics of brain pathophysiology. However, the value of T1-weighted images, quantitative susceptibility mapping, and R2* mapping in the diagnosis of Parkinson's disease (PD) was underestimated in previous studies. In this prospective study to establish a model for PD diagnosis based on brain imaging information, we collected high-resolution T1-weighted images, R2* mapping, and quantitative susceptibility imaging data from 171 patients with PD and 179 healthy controls recruited from August 2014 to August 2019. According to the inclusion time, 123 PD patients and 121 healthy controls were assigned to train the diagnostic model, while the remaining 106 subjects were assigned to the external validation dataset. We extracted 1408 radiomics features, and then used data-driven feature selection to identify informative features that were significant for discriminating patients with PD from normal controls on the training dataset. The informative features so identified were then used to construct a diagnostic model for PD. The constructed model contained 36 informative radiomics features, mainly representing abnormal subcortical iron distribution (especially in the substantia nigra), structural disorganization (e.g., in the inferior temporal, paracentral, precuneus, insula, and precentral gyri), and texture misalignment in the subcortical nuclei (e.g., caudate, globus pallidus, and thalamus). The predictive accuracy of the established model was 81.1 ± 8.0% in the training dataset. On the external validation dataset, the established model showed predictive accuracy of 78.5 ± 2.1%. In the tests of identifying early and drug-naïve PD patients from healthy controls, the accuracies of the model constructed on the same 36 informative features were 80.3 ± 7.1% and 79.1 ± 6.5%, respectively, while the accuracies were 80.4 ± 6.3% and 82.9 ± 5.8% for diagnosing middle-to-late PD and those receiving drug management, respectively. The accuracies for predicting tremor-dominant and non-tremor-dominant PD were 79.8 ± 6.9% and 79.1 ± 6.5%, respectively. In conclusion, the multiple-tissue-specific brain radiomics model constructed from magnetic resonance imaging has the ability to discriminate PD and exhibits the advantages for improving PD diagnosis.

Cover page of Workshop-based learning and networking: a scalable model for research capacity strengthening in low- and middle-income countries.

Workshop-based learning and networking: a scalable model for research capacity strengthening in low- and middle-income countries.

(2022)

Science education and research have the potential to drive profound change in low- and middle-income countries (LMICs) through encouraging innovation, attracting industry, and creating job opportunities. However, in LMICs, research capacity is often limited, and acquisition of funding and access to state-of-the-art technologies is challenging. The Alliance for Global Health and Science (the Alliance) was founded as a partnership between the University of California, Berkeley (USA) and Makerere University (Uganda), with the goal of strengthening Makerere University's capacity for bioscience research. The flagship program of the Alliance partnership is the MU/UCB Biosciences Training Program, an in-country, hands-on workshop model that trains a large number of students from Makerere University in infectious disease and molecular biology research. This approach nucleates training of larger and more diverse groups of students, development of mentoring and bi-directional research partnerships, and support of the local economy. Here, we describe the project, its conception, implementation, challenges, and outcomes of bioscience research workshops. We aim to provide a blueprint for workshop implementation, and create a valuable resource for bioscience research capacity strengthening in LMICs.

Cover page of Electrically regenerated ion-exchange technology: Leveraging faradaic reactions and assessing the effect of co-ion sorption

Electrically regenerated ion-exchange technology: Leveraging faradaic reactions and assessing the effect of co-ion sorption

(2022)

Capacitive deionization (CDI) technologies have the potential to become cost-competitive alternatives to reverse osmosis for the treatment of brackish waters. In this study, we report our findings on the effect of co-ion sorption and faradaic side reactions on our ion exchange resin functionalized desalination electrodes which passively capture salt and reject it upon charging. This system, which we previously reported on and refer to as electrically regenerated ion exchange (ERI), avoids the use of expensive ion exchange membranes in an effort to save costs. Surprisingly, we find that, compared to a reference CDI system, ERI electrodes capture salt most effectively at low applied voltages (0.5 mg/cm3 at 0.8 V). Both CDI and ERI systems also seem to suffer from co-ion sorption effects which negatively impact salt adsorption. However, Faradaic side reactions at higher voltages (1 V and 1.2 V) which we track via pH measurements, serve as a detriment to CDI but seem to facilitate the functionality of ERI.