Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance.

Advanced computational methods are being actively sought to address the challenges associated with the discovery and development of new combinatorial materials, such as formulations. A widely adopted approach involves domain-informed high-throughput screening of individual components that can be combined together to form a formulation. This manages to accelerate the discovery of new compounds for a target application but still leaves the process of identifying the right "formulation" from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, the Formulation Graph Convolution Network (F-GCN), that can map the structure-composition relationship of the formulation constituents to the property of liquid formulation as a whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on the respective constituent's molar percentage in the formulation, followed by integration into a combined formulation descriptor that represents the complete formulation to an external learning architecture. The use case of the proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary data sets representing electrolyte formulations vs battery performance: one data set is sourced from the literature about Li/Cu half-cells, while the other is obtained by lab experiments related to lithium-iodide full-cell chemistry. The model is shown to predict performance metrics such as Coulombic efficiency (CE) and specific capacity of new electrolyte formulations with the lowest reported errors. The best-performing F-GCN model uses molecular descriptors derived from molecular graphs (GCNs) that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.

A review of functional pancreatic neuroendocrine tumors: Exploring the molecular pathogenesis, diagnosis and treatment.

Pancreatic neuroendocrine tumors (PanNETs) are a rare subtype of pancreatic cancer and can be divided into functional (30-40%) and nonfunctional subtypes. The different subtypes of functional PanNETs (F-PanNETs) have a variety of classical presentations that raise suspicion for an underlying PanNET. It is estimated that 90% of PanNETs are sporadic, and the PI3K-Akt-mTOR and ATRX/DAXX signaling pathways have been recognized as key genetic pathways implicated in the pathogenesis. The other 10% of PanNETs may occur in the context of familial cancer syndromes such as MEN1. Chromogranin A is the most useful biomarker currently; however, several studies have shown limitations with its use, especially its prognostic value. Synaptophysin is a novel biomarker which has shown promising preliminary results however its use clinically has yet to be established. Blood tests assessing hormone levels, cross-sectional imaging, and endoscopic ultrasound remain at the core of establishing a diagnosis of F-PanNET. The treatment options for F-PanNETs include surgical methods such as enucleation, systemic therapies like chemotherapy and novel targeted therapies such as everolimus. The prognosis for F-PanNETs is more favorable than for nonfunctional PanNETs, however metastatic disease is associated with poor survival outcomes. Researchers should also focus their efforts on identifying novel pathways implicated in the pathogenesis of F-PanNETs in order to develop new targeted therapies that may reduce the need for surgical intervention and on the establishment of novel biomarkers that may reduce the need for invasive testing and allow for earlier detection of F-PanNETs.

Stochastic and mixed density functional theory within the projector augmented wave formalism for simulation of warm dense matter.

Stochastic density functional theory (DFT) and mixed stochastic-deterministic DFT are burgeoning approaches for the calculation of the equation of state and transport properties in materials under extreme conditions. In the intermediate warm dense matter regime, a state between correlated condensed matter and kinetic plasma, electrons can range from being highly localized around nuclei to delocalized over the whole simulation cell. The plane-wave basis pseudopotential approach is thus the typical tool of choice for modeling such systems at the DFT level. Unfortunately, stochastic DFT methods scale as the square of the maximum plane-wave energy in this basis. To reduce the effect of this scaling and improve the overall description of the electrons within the pseudopotential approximation, we present stochastic and mixed DFT approaches developed and implemented within the projector augmented wave formalism. We compare results between the different DFT approaches for both single-point and molecular dynamics trajectories and present calculations of self-diffusion coefficients of solid density carbon from 1 to 50 eV.

Computational nodal displacement analysis of acetabulum fossa for injection molded cemented polyethylene acetabular liner.

The acetabular liner (AL) is one of the key components that determine the functionality and durability of the total hip joint replacement (THR) device. The performance of Ultra high molecular weight polyethylene (UHMWPE)-based AL depends critically on the manufacturing route and its properties, which are evaluated pre-clinically using a host of experimental and computational analyses. The conventional manufacturing of an AL involves multiple stages, including extrusion/compression molding followed by machining, which is time/cost intensive and leads to material loss. In such a scenario, injection molding is a promising alternative, yet its feasbility remains unexplored for the manufacturing of AL for THA applications. Against this backdrop, the two-fold objectives of this work are to report our recent efforts to establish the efficacy of the injection molding of new generation UHMWPE biomaterial; HU (60 wt% HDPE- 40 wt% UHMWPE blend) for manufacturing AL prototype and to present the key biomechanical response analysis of this prototype, in silico. A range of manufacturing relevant material properties, as well as customized mold design to manufacture HU-based AL with external design features, are discussed. Such guidelines are particularly relevant to mold polymeric parts with a higher thickness (>8 mm). As part of the pre-clinical validation of AL with new design features, a less explored in silico approach to assess biomechanical micro-strain in the acetabulum fossa is presented, and the results are analysed in accordance with the mechanostat theory. The outcomes revealed that for a 100 kg subject weight, average micro-strain in the remodelling region was 1132, while it was determined as 723 for a 55 kg subject weight. Such results highlight the influence of subject weight on micro-strain generation and distribution in the acetabulum fossa. The von Mises stress in AL also increased with subject weight from 17 MPa in a subject weight of 55 kg to 28 MPa in a subject weight of 100 kg. Taken together, this work demonstrates the feasibility and competence of this new generation biomaterial in terms of implant manufacturing via injection molding with a clinically desired biomechanical response.

Assessing medical students' perception and educational experience during COVID-19 pandemic.

INTRODUCTION: The COVID-19 pandemic has significantly impacted the traditional delivery of medical education. Medical education programmes have had to cope with limitations on face-to-face learning, and accelerate the adoption of digital learning. In addition, the pandemic has potential serious implications on the psychological well-being of medical students. We aim to assess the changes in perceptions and experiences of medical students as a consequence of this pandemic. METHODS: Cross-sectional survey of medical students at Trinity College Dublin (TCD) between March and April 2022 was performed. The survey explored student satisfaction with the current education program, teaching delivery and the impact of COVID-19 on education and student well-being. RESULTS: 175 medical students participated in the survey. Overall, the majority of students were happy/neutral with their medical education. 93 (53.1%) felt tutorials and problem-based learning (PBL) to be the most effective method of teaching, followed by laboratory and clinical placements in 78 participants (44.6%) and hybrid-learning in 85 participants (48.6%). There was a mixed reaction to the changes in the delivery of education brought about by the pandemic. 67 participants (40.6%) felt happy with the changes, another 64 participants (38.8%) felt neutral, whilst only 34 participants (20.6%) were unhappy. However, most participants felt the pandemic negatively impacted their mental health, with 96 participants (55.8%) reporting negative responses. 58% of participants (n = 102/175) reported utilising the student support services at university campus and 49% (n = 50) were satisfied with their services. CONCLUSION: Digital content and delivery confer the benefit of greater flexibility in learning, the ability to learn at one's own pace and in a preferred environment, however lacks the advantage of bedside teaching and hands-on training. Our findings reinforce the potential advantages of online learning.

Probing lubricated sliding wear properties of HDPE/UHMWPE hybrid bionanocomposite.

Ultra-high molecular weight polyethylene (UHMWPE) and its derivatives have been clinically used as an acetabular liner material in total hip joint replacement (THR) over last six decades. Despite significant efforts, the longevity of UHMWPE implants is still impaired due to their compromised tribological performance, leading to osteolysis and aseptic loosening. The present study aims to critically evaluate and analyze the tribological performance, of the next generation acetabular liner material, that is, a chemically modified graphene oxide (GO) reinforced HDPE/UHMWPE (HU) bionanocomposite (HUmGO), against stainless steel (SS 316L) counterface in lubricated conditions. This work also provides a performance comparative assessment of HUmGO with respect to medical grades, UHMWPE (UC) and crosslinked UHMWPE (XL-UC). Significant attempts have been made to correlate the tribological properties (frictional behavior, wear rate, wear debris shape and size, wear mechanism) with the physicomechanical conditions (contact stresses) at sliding contact and the variation in molecular architecture of different UHMWPE materials. Additionally, an emphasis is put forward to critically anlyze the nature of lubrication regime based on the bearing characterstic parameters. HUmGO exhibited a lower COF (0.07) and specific wear rate (2.86 × 10-8 mm3/Nm) than UC and XL-UC under identical sliding conditions. The worn surfaces on HUmGO revealed the signatures of less abrasive wear and limited deformation. Based on the estimated lambda (λ) ratio and Sommerfield number, all the investigated sliding contacts exhibited boundary lubrication. Taken together, the modified GO reinforced HDPE/UHMWPE bionanocomposite can be considered as a new generation biomaterial for the fabrication of acetabular liner for hip-joint prosthesis.

Polydopamine Codoped BaTiO3-Functionalized Polyvinylidene Fluoride Coating as a Piezo-Biomaterial Platform for an Enhanced Cellular Response and Bioactivity.

For a number of clinical applications, Ti6Al4V implants with bioactive coatings are used. However, the deposition of a functional polymeric coating with desired physical properties, biocompatibility, and long-term stability remains largely unexplored. Among widely investigated synthetic biomaterials, polyvinylidene fluoride (PVDF) with ß-polymorph and barium titanate (BaTiO3, BT) are considered as good examples of piezo-biopolymers and bioceramics, respectively. In this work, an adherent PVDF-based nanocomposite coating is deposited onto a Ti6Al4V substrate to explore the impact of its functional characteristics (piezoactivity) on cellular behavior and bioactivity (apatite growth and mineralized matrix formation). The precursor solution was prepared by physically grafting PVDF with polydopamine (pDOPA), forming mPVDF. Subsequently, mPVDF was reinforced with BaTiO3 nanoparticles in dimethylformamide/acetone solution, and the resulting nanocomposite (mPVDF-BT) was then spray-coated onto a roughened Ti6Al4V substrate using an airbrush at 140 °C under a pressure of 2 bar. The reproducibility of this simple yet effective processing approach to deposit chemically stable and adherent coatings was established. Remarkably, the modification with pDOPA and reinforcement with BaTiO3 nanoparticles resulted in an enhanced ß-fraction of PVDF up to 96%. This nanocomposite encouraged cellular viability of preosteoblasts (∼158% at day 5) and characteristic spreading, in vitro. Our findings indicate that the mPVDF-BT coating facilitated faster nucleation and growth of the biomineralized apatite layer with ∼70% coverage within 3 days of incubation in the simulated body fluid. In addition, the coupling among surface polar energy (5.5 mN/m), fractional polarity (∼117%), roughness (8.7 µm), and fibrous morphology also endorsed better cellular behavior. Taken together, this coating deposition strategy will pave the pathway toward designing cell-instructive surface-modified Ti6Al4V biomaterials with tailored biomineralization and bioactivity properties for musculoskeletal reconstruction and regeneration applications.

Variation in the interface strength of silicon with surface engineered Ti3C2 MXenes.

Current advancements in battery technologies require electrodes to combine high-performance active materials such as Silicon (Si) with two-dimensional materials such as transition metal carbides (MXenes) for prolonged cycle stability and enhanced electrochemical performance. More so, it is the interface between these materials, which is the nexus for their applicatory success. Herein, the interface strength variations between amorphous Si and Ti3C2Tx MXenes are determined as the MXene surface functional groups (Tx) are changed using first principles calculations. Si is interfaced with three Ti3C2 MXene substrates having surface -OH, -OH and -O mixed, and -F functional groups. Density functional theory (DFT) results reveal that completely hydroxylated Ti3C2 has the highest interface strength of 0.6 J m-2 with amorphous Si. This interface strength value drops as the proportion of surface -O and -F groups increases. Additional analysis of electron redistribution and charge separation across the interface is provided for a complete understanding of underlying physico-chemical factors affecting the surface chemistry and resultant interface strength values. The presented comprehensive analysis of the interface aims to develop sophisticated MXene based electrodes by their targeted surface engineering.

Understanding the Strength of the Selenium-Graphene Interfaces for Energy Storage Systems.

We present comprehensive first-principles density functional theory (DFT) analyses of the interfacial strength and bonding mechanisms between crystalline and amorphous selenium (Se) with graphene (Gr), a promising duo for energy storage applications. Comparative interface analyses are presented on amorphous silicon (Si) with graphene and crystalline Se with a conventional aluminum (Al) current collector. The interface strengths of monoclinic Se (0.43 J m-2) and amorphous Si with graphene (0.41 J m-2) are similar in magnitude. While both materials (c-Se, a-Si) are bonded loosely by van der Waals (vdW) forces over graphene, interfacial electron exchange is higher for a-Si/graphene. This is further elaborated by comparing the potential energy step and charge transfer (Δq) across the graphene interfaces. The interface strength of c-Se on a 3D Al current collector is higher (0.99 J m-2), suggesting a stronger adhesion. Amorphous Se with graphene has comparable interface strength (0.34 J m-2), but electron exchange in this system is slightly distinct from monoclinic Se. The electronic characteristics and bonding mechanisms are different for monoclinic and amorphous Se with graphene as they activate graphene via surface charge doping divergently. The implications of these interfacial physicochemical attributes on electrode performance have been discussed. Our findings highlight the complex electrochemical phenomena in Se interfaced with graphene, which may profoundly differ from their "free" counterparts.