Advancing post-traumatic seizure classification and biomarker identification: Information decomposition based multimodal fusion and explainable machine learning with missing neuroimaging data.

A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions. A supervised binary classification task was created, contrasting data from LPTS patients with those without LPTS. To accommodate missing modalities in some subjects, we took a two-pronged approach. Firstly, we extended a graphical model-based Bayesian estimator to directly classify subjects with incomplete modality. Secondly, we explored conventional imputation techniques. The imputed multimodal information was then combined, following several fusion and dimensionality reduction techniques found in the literature, and subsequently fitted to a kernel- or a tree-based classifier. For this fusion, we proposed two new algorithms: recursive elimination of correlated components (RECC) that filters information based on the correlation between the already selected features, and information decomposition and selective fusion (IDSF), which effectively recombines information from decomposed multimodal features. Our cross-validation findings showed that the proposed IDSF algorithm delivers superior performance based on the area under the curve (AUC) score. Ultimately, after rigorous statistical comparisons and interpretable machine learning examination using Shapley values of the most frequently selected features, we recommend the two following magnetic resonance imaging (MRI) abnormalities as potential biomarkers: the left anterior limb of internal capsule in diffusion MRI (dMRI), and the right middle temporal gyrus in functional MRI (fMRI).

M2M-InvNet: Human Motor Cortex Mapping From Multi-Muscle Response Using TMS and Generative 3D Convolutional Network.

Transcranial magnetic stimulation (TMS) is often applied to the motor cortex to stimulate a collection of motor evoked potentials (MEPs) in groups of peripheral muscles. The causal interface between TMS and MEP is the selective activation of neurons in the motor cortex; moving around the TMS 'spot' over the motor cortex causes different MEP responses. A question of interest is whether a collection of MEP responses can be used to identify the stimulated locations on the cortex, which could potentially be used to then place the TMS coil to produce chosen sets of MEPs. In this work we leverage our previous report on a 3D convolutional neural network (CNN) architecture that predicted MEPs from the induced electric field, to tackle an inverse imaging task in which we start with the MEPs and estimate the stimulated regions on the motor cortex. We present and evaluate five different inverse imaging CNN architectures, both conventional and generative, in terms of several measures of reconstruction accuracy. We found that one architecture, which we propose as M2M-InvNet, consistently achieved the best performance.

Integrated photovoltaic-thermal system utilizing front surface water cooling technique: An experimental performance response.

In the realm of photovoltaic-thermal (PVT) systems, optimizing operating temperatures for photovoltaic (PV) panels is a challenge. This study introduces a novel solution: a sprayed water PVT system that simultaneously harnesses energy and electricity. The aim is twofold: generate electricity through PV panels and produce hot water via a flat plate collector, using an innovative cooling mechanism. Water sprayed onto the PV panel's surface flows to a collector for storage. With varied flow rates, optimal panel efficiency occurs at a 45° tilt angle, accompanied by lower collector outlet temperatures at higher flow rates. The collector achieves a peak thermal efficiency of 70.6 %, producing hot water at 84.6 °C. Notably, a significant PV panel efficiency enhancement, up to 16.78 %, especially at 1.56 L/min flow rate, is observed. The cooling technique consistently reduces panel temperatures from 45.08 °C to 34.12 °C. A self-cleaning spray mechanism improves efficiency by 2.53 %, resulting in an overall system efficiency of 83.3 %. This research offers an innovative approach to enhance energy generation and electricity in PVT systems, promising sustainable energy optimization.

Analysis of Distance of Frontal Sinus Ostium from Columella by CT PNS: Our Experience.

Introduction: Knowledge of variable anatomy, narrow frontal sinus ostium and vital anatomical structures near outflow tract, is very important during preoperative planning for exposure of the frontal sinus recess during endoscopic sinus surgery. Preoperative knowledge of distance of nasofrontal beak and anterior skull base from columella is very helpful in avoiding intraoperative complication by deeper penetration into cranial cavity. Aim: To analyse distance from columella to the anterior and posterior border of the Frontal sinus ostium in males and females by CT PNS. Materials and methods: This retrospective observational study carried out in katihar medical college, Katihar during the period of 01 July 2021 to 31 December 2021 including 31 patients. Result and Conclusion: A distance approx 60.9 mm in men and 57.34 mm in women from the columella to frontal sinus ostium is safe during endoscopic sinus surgery.

Identification and Quantification of Aqueous Disinfectants Using an Array of Carbon Nanotube-Based Chemiresistors.

Disinfection of water is essential to prevent the growth of pathogens, but at high levels, it can cause harm to human health. Therefore, accurate monitoring of disinfectant concentrations in water is essential to ensure safe drinking water. The use of multiple disinfectants at different stages in water treatment plants makes it necessary to also identify the type and concentrations of all of the disinfectant species present. Here, we demonstrate an effective approach to identify and quantify multiple disinfectants (using the example of free chlorine and potassium permanganate) in water using single-walled carbon nanotube (SWCNT)-based reagent-free chemiresistive sensing arrays. Facile fabrication of chemiresistive devices makes them a popular choice for the implementation of sensor arrays. Our sensing array consists of functionalized and unfunctionalized (blank) SWCNT sensors to distinguish the disinfectants. The distinct responses from the different sensors at varying concentrations and pH can be fitted to the mathematical model of a Langmuir adsorption isotherm separately for each sensor. Blank and functionalized sensors respond through different mechanisms that result in varying responses that are concentration- and pH-dependent. Chemometric techniques such as principal component analysis (PCA) and partial least-squares-discriminant analysis (PLS-DA) were used to analyze the sensor data. PCA showed an excellent separation of the analytes over five different pHs (5.5, 6.5, 7.5, 8.5, and 9.5). PLS-DA provided excellent separability as well as good predictability with a Q2 of 94.26% and an R2 of 95.67% for the five pH regions of the two analytes. This proof-of-concept solid-state chemiresistive sensing array can be developed for specific disinfectants that are commonly used in water treatment plants and can be deployed in water distribution and monitoring facilities. We have demonstrated the applicability of chemiresistive devices in a sensor array format for the first time for aqueous disinfectant monitoring.

In Vitro Antibacterial, Cytotoxicity and Wound Healing Activities of Methanol and Aqueous Extracts from Achyranthes aspera.

Achyranthes aspera (Amaranthaceae) is a most important plant in the Indian medicinal system of Unani, Ayurveda, and Sidha. It is distributed throughout tropical countries particularly this medicinal herb is found as a weed all over India. The antibiotic-resistant bacteria treatment, many medicinal plants act as alternatives to antibiotics and are considered new resources for producing agents. The aim of this study was to evaluate the antioxidant, antibacterial, cytotoxicity, and wound-healing potential of methanolic and aqueous extracts of A. aspera. Physio-chemical parameters of A. aspera were screened by qualitative and quantitative phytochemicals and it revealed the existence of saponins, alkaloids, glycosides, and flavonoids in each extract. The methanolic extract showed strong scavenging effects against 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical in comparison with the aqueous extract. The methanolic extract of A. aspera was bactericidal at 100 µg/ml concentration against Gram-negative (Escherichia coli, Pseudomonas aeruginosa) and Gram-positive bacteria (Bacillus subtilis, Staphylococcus aureus). In addition, the finding suggests A. aspera extract promotes in vitro wound healing by enhancement of Vero cells and has the potential for the treatment of wounds. The study concludes the strong radical scavenging effect of Achyranthes aspera along with remarkable antibacterial and wound healing actions.

A reagent-free phosphate chemiresistive sensor using carbon nanotubes functionalized with crystal violet.

Phosphate is important for plant and animal growth. Therefore, it is commonly added as a fertilizer in agricultural fields. Phosphorus is typically measured using colorimetric or electrochemical sensors. Colorimetric sensors suffer from a limited measuring range and toxic waste generation while electrochemical sensors suffer from long-term drifts due to reference electrodes. Here, we propose a solid-state, reagent-free and reference electrode-free chemiresistive sensor for measuring phosphate using single-walled carbon nanotubes functionalized with crystal violet. The functionalized sensor exhibited a measuring range from 0.1 mM to 10 mM at pH 8. No significant interference was observed for common interfering anions like nitrates, sulphates, and chlorides. This study showed a proof-of-concept chemiresistive sensor, which can potentially be used to measure phosphate levels in hydroponics and aquaponics systems. The dynamic measuring range further needs to be extended for surface water samples.

Anisotropic crystal orientations dependent mechanical properties and fracture mechanisms in zinc blende ZnTe nanowires.

The orientations of crystal growth significantly affect the operating characteristics of elastic and inelastic deformation in semiconductor nanowires (NWs). This work uses molecular dynamics simulation to extensively investigate the orientation-dependent mechanical properties and fracture mechanisms of zinc blende ZnTe NWs. Three different crystal orientations, including [100], [110], and [111], coupled with temperatures (100 to 600 K) on the fracture stress and elastic modulus, are thoroughly studied. In comparison to the [110] and [100] orientations, the [111]-oriented ZnTe NW exhibits a high fracture stress. The percentage decrease in fracture strength exhibits a pronounced variation with increasing temperature, with the highest magnitude observed in the [100] direction and the lowest magnitude observed in the [110] direction. The elastic modulus dropped by the largest percentage in the [111] direction as compared to the [100] direction. Most notably, the [110]-directed ZnTe NW deforms unusually as the strain rate increases, making it more sensitive to strain rate than other orientations. The strong strain rate sensitivity results from the unusual short-range and long-range order crystals appearing due to dislocation slipping and partial twinning. Moreover, the {111} plane is the principal cleavage plane for all orientations, creating a dislocation slipping mechanism at room temperature. The {100} plane becomes active and acts as another fundamental cleavage plane at increasing temperatures. This in-depth analysis paves the way for advancing efficient and reliable ZnTe NWs-based nanodevices and nanomechanical systems.

Substantial increase in stillbirth rate during the COVID-19 pandemic: results from a population-based study in the Indian state of Bihar.

INTRODUCTION: We report on the stillbirth rate (SBR) and associated risk factors for births during the COVID-19 pandemic, and change in SBR between prepandemic (2016) and pandemic periods in the Indian state of Bihar. METHODS: Births between July 2020 and June 2021 (91.5% participation) representative of Bihar were listed. Stillbirth was defined as fetal death with gestation period of ≥7 months where the fetus did not show any sign of life. Detailed interviews were conducted for all stillbirths and neonatal deaths, and for 25% random sample of surviving live births. We estimated overall SBR, and during COVID-19 peak and non-peak periods per 1000 births. Multiple logistic regression models were run to assess risk factors for stillbirth. The change in SBR for Bihar from 2016 to 2020-2021 was estimated. RESULTS: We identified 582 stillbirths in 30 412 births with an estimated SBR of 19.1 per 1000 births (95% CI 17.7 to 20.7); SBR was significantly higher in private facility (38.4; 95% CI 34.3 to 43.0) than in public facility (8.6; 95% CI 7.3 to 10.1) births, and for COVID-19 peak (21.2; 95% CI 19.2 to 23.4) than non-peak period (16.3; 95% CI 14.2 to 18.6) births. Pregnancies with the last pregnancy trimester during the COVID-19 peak period had 40.4% (95% CI 10.3% to 70.4%) higher SBR than those who did not. Risk factor associations for stillbirths were similar between the COVID-19 peak and non-peak periods, with gestation age of <8 months with the highest odds of stillbirth followed by referred deliveries and deliveries in private health facilities. A statistically significant increase of 24.3% and 68.9% in overall SBR and intrapartum SBR was seen between 2016 and 2020-2021, respectively. CONCLUSIONS: This study documented an increase in SBR during the COVID-19 pandemic as compared with the prepandemic period, and the varied SBR based on the intensity of the COVID-19 pandemic and by the place of delivery.

Virosome: An engineered virus for vaccine delivery.

The purpose of immunization is the effective cellular and humoral immune response against antigens. Several studies on novel vaccine delivery approaches such as micro-particles, liposomes & nanoparticles, etc. against infectious diseases have been investigated so far. In contrast to the conventional approaches in vaccine development, a virosomes-based vaccine represents the next generation in the field of immunization because of its balance between efficacy and tolerability by virtue of its mechanism of immune instigation. The versatility of virosomes as a vaccine adjuvant, and delivery vehicle of molecules of different nature, such as peptides, nucleic acids, and proteins, as well as provide an insight into the prospect of drug targeting using virosomes. This article focuses on the basics of virosomes, structure, composition formulation and development, advantages, interplay with the immune system, current clinical status, different patents highlighting the applications of virosomes and their status, recent advances, and research associated with virosomes, the efficacy, safety, and tolerability of virosomes based vaccines and the future prospective.

Assessment of quality of antenatal care services in public sector facilities in India.

OBJECTIVES: We undertook assessment of quality of antenatal care (ANC) services in public sector facilities in the Indian state of Bihar state delivered under the national ANC programme (Pradhan Mantri Surakshit Matritva Abhiyan, PMSMA). SETTING: Three community health centres and one subdistrict hospital each in two randomly selected districts of Bihar. PARTICIPANTS: Pregnant women who sought ANC services under PMSMA irrespective of the pregnancy trimester. PRIMARY AND SECONDARY MEASURES: Quality ANC services were considered if a woman received all of these services in that visit-weight, blood pressure and abdomen check, urine and blood sample taken, and were given iron and folic acid and calcium tablets. The process of ANC service provision was documented. RESULTS: Eight hundred and fourteen (94.5% participation) women participated. Coverage of quality ANC services was 30.4% (95% CI 27.3% to 33.7%) irrespective of pregnancy trimester, and was similar in both districts and ranged 3%-83.1% across the facilities. Quality ANC service coverage was significantly lower for women in the first trimester of pregnancy (6.8%, 95% CI 3.3% to 13.6%) as compared with those in the second (34.4%, 95% CI 29.9% to 39.1%) and third (32.9%, 95% CI 27.9% to 38.3%) trimester of pregnancy. Individually, the coverage of weight and blood pressure check-up, receipt of iron folic acid (IFA) and calcium tablets, and blood sample collection was >85%. The coverage of urine sample collection was 46.3% (95% CI 42.9% to 49.7%) and of abdomen check-up was 62% (95% CI 58.6% to 65.3%). Poor information sharing post check-up was done with the pregnant women. Varied implementation of ANC service provision was seen in the facilities as compared with the PMSMA guidelines, in particular with laboratory diagnostics and doctor consultation. Task shifting from doctors to ANMs was observed in all facilities. CONCLUSIONS: Grossly inadequate quality ANC services under the PMSMA needs urgent attention to improve maternal and neonatal health outcomes.

PENet: Continuous-Valued Pulmonary Edema Severity Prediction On Chest X-ray Using Siamese Convolutional Networks.

For physicians to make rapid clinical decisions for patients with congestive heart failure, the assessment of pulmonary edema severity in chest radiographs is vital. Although deep learning has shown promise in detecting the presence or absence or discrete grades of severity, of such edema, prediction of continuous-valued severity yet remains a challenge. Here, we propose PENet: Siamese convolutional neural networks to assess the continuous spectrum of severity of lung edema from chest radiographs. We present different modes of implementing this network and demonstrate that our best model outperforms that of earlier work (mean AUC of 0.91 over 0.87), while using only 1/16-th the dimension of input images and 1/69-th the size of training data, thus also saving expensive computation.

Implications of the availability and distribution of birth weight on addressing neonatal mortality: population-based assessment from Bihar state of India.

OBJECTIVE: A large proportion of neonatal deaths in India are attributable to low birth weight (LBW). We report population-based distribution and determinants of birth weight in Bihar state, and on the perceptions about birth weight among carers. DESIGN: A cross-sectional household survey in a state representative sample of 6007 live births born in 2018-2019. Mothers provided detailed interviews on sociodemographic characteristics and birth weight, and their perceptions on LBW (birth weight <2500 g). We report on birth weight availability, LBW prevalence, neonatal mortality rate (NMR) by birth weight and perceptions of mothers on LBW implications. SETTING: Bihar state, India. PARTICIPANTS: Women with live birth between October 2018 and September 2019. RESULTS: A total of 5021 (83.5%) live births participated, and 3939 (78.4%) were weighed at birth. LBW prevalence among those with available birth weight was 18.4% (95% CI 17.1 to 19.7). Majority (87.5%) of the live births born at home were not weighed at birth. LBW prevalence decreased and birth weight ≥2500 g increased significantly with increasing wealth index quartile. NMR was significantly higher in live births weighing <1500 g (11.3%; 95% CI 5.1 to 23.1) and 1500-1999 g (8.0%; 95% CI 4.6 to 13.6) than those weighing ≥2500 g (1.3%, 95% CI 0.9 to 1.7). Assuming proportional correspondence of LBW and NMR in live births with and without birth weight, the estimated LBW among those without birth weight was 35.5% (95% CI 33.0 to 38.0) and among all live births irrespective of birth weight availability was 23.0% (95% CI 21.9 to 24.2). 70% of mothers considered LBW to be a sign of sickness, 59.5% perceived it as a risk of developing other illnesses and 8.6% as having an increased probability of death. CONCLUSIONS: Missing birth weight is substantially compromising the planning of interventions to address LBW at the population-level. Variations of LBW by place of delivery and sociodemographic indicators, and the perceptions of carers about LBW can facilitate appropriate actions to address LBW and the associated neonatal mortality.

Defect Density-Dependent pH Response of Graphene Derivatives: Towards the Development of pH-Sensitive Graphene Oxide Devices.

In this study, we demonstrate that a highly pH-sensitive substrate could be fabricated by controlling the type and defect density of graphene derivatives. Nanomaterials from single-layer graphene resembling a defect-free structure to few-layer graphene and graphene oxide with high defect density were used to demonstrate the pH-sensing mechanisms of graphene. We show the presence of three competing mechanisms of pH sensitivity, including the availability of functional groups, the electrochemical double layer, and the ion trapping that determines the overall pH response. The graphene surface was selectively functionalized with hydroxyl, amine, and carboxyl groups to understand the role and density of the graphene pH-sensitive functional groups. Later, we establish the development of highly pH-sensitive graphene oxide by controlling its defect density. This research opens a new avenue for integrating micro-nano-sized pH sensors based on graphene derivatives into next-generation sensing platforms.

Tensile Mechanical Behavior and the Fracture Mechanism in Monolayer Group-III Nitrides XN (X= Ga, In): Effect of Temperature and Point Vacancies.

In this study, we have thoroughly investigated the tensile mechanical behavior of monolayer XN (X = Ga, In) using molecular dynamics simulations. The effects of temperature (100 to 800 K) and point vacancies (PVs, 0.1 to 1%) on fracture stress, strain, and elastic modulus of GaN and InN are studied. The effects of edge chiralities on the tensile mechanical behavior of monolayer XN are also explored. We find that the elastic modulus, tensile strength, and fracture strain reduce with increasing temperature. The point defects cause the stress to be condensed in the vicinity of the vacancies, resulting in straightforward damage. On the other hand, all the mechanical behaviors such as fracture stress, elastic modulus, and fracture strain show substantial anisotropic nature in these materials. To explain the influence of temperature and PVs, the radial distribution function (RDF) at diverse temperatures and potential energy/atom at different vacancy concentrations are calculated. The intensity of the RDF peaks decreases with increasing temperature, and the presence of PVs leads to an increase in potential energy/atom. The current work provides an insight into adjusting the tensile mechanical behaviors by making vacancy defects in XN (X = Ga, In) and provides a guideline for the applications of XN (X = Ga, In) in flexible nanoelectronic and nanoelectromechanical devices.

Detection of free chlorine in water using graphene-like carbon based chemiresistive sensors.

Free chlorine is the most commonly used water disinfectant. Measuring its concentration during and after water treatment is crucial to ensure its effectiveness. However, many of the existing methods do not allow for continuous on-line monitoring. Here we demonstrate a solid state chemiresistive sensor using graphene-like carbon (GLC) that overcomes that issue. GLC films that were either bare or non-covalently functionalized with the redox-active phenyl-capped aniline tetramer (PCAT) were successfully employed to quantify aqueous free chlorine, although functionalized devices showed better performance. The response of the sensors to increasing concentrations of free chlorine followed a Langmuir adsorption isotherm in the two tested ranges: 0.01-0.2 ppm and 0.2-1.4 ppm. The limit of detection was estimated to be 1 ppb, permitting the detection of breaches in chlorine filters. The devices respond to decreasing levels of free chlorine without the need for a reset, allowing for the continuous monitoring of fluctuations in the concentration. The maximum sensor response and saturation concentration were found to depend on the thickness of the GLC film. Hence, the sensitivity and dynamic range of the sensors can be tailored to different applications by adjusting the thickness of the films. Tap water samples from a residential area were tested using these sensors, which showed good agreement with standard colorimetric measurement methods. The devices did not suffer from interferences in the presence of ions commonly found in drinking water. Overall, these sensors are a cost-effective option for the continuous automated monitoring of free chlorine in drinking water.

Vacancy-Induced Thermal Transport and Tensile Mechanical Behavior of Monolayer Honeycomb BeO.

Because of the rapid shrinking trend of integrated circuits, the performances of nanodevices and nanomechanical systems are greatly affected by the joule heating and mechanical failure dilemma. In addition, structural defects are inevitable during experimental synthesis of nanomaterials, which may alter their physical properties significantly. Investigation of the thermal transport and mechanical behavior of nanostructured materials with structural defects is thus a crucial requirement. In this study, the thermal conductivity (TC) and tensile mechanical behavior of monolayer honeycomb BeO are systematically explored using molecular dynamics simulations. An infinite length bulk TC of ∼277.77 ± 8.93 W/mK was found for the pristine monolayer BeO. However, the insertion of 1% single vacancy (SV) and double vacancy (DV) defects reduces the TC by ∼36.98 and ∼33.52%, respectively. On the other hand, the uniaxial tensile loading produces asymmetrical fracture stress, elastic modulus, and fracture strain behaviors in the armchair and zigzag directions. The elastic modulus was reduced by ∼4.7 and ∼6.6% for 1% SV defects along the armchair and zigzag directions, respectively, whereas the reduction was ∼2.7 and ∼ 5.1% for 1% DV defects. Moreover, because of the strong symmetry-breaking effect, both the TC and mechanical strength were significantly lower for the SV defects than those for the DV defects. The highly softening and decreasing trends of the phonon modes with increasing vacancy concentration and temperature, respectively, were noticed for both types of defects, resulting in a reduction of the TC of the defected structures. These findings will be helpful for the understanding of the heat transport and mechanical characteristics of monolayer BeO as well as provide guidance for the design and control of BeO-based nanoelectronic and nanoelectromechanical devices.

Neuroinflammation: A Potential Risk for Dementia.

Dementia is a neurodegenerative condition that is considered a major factor contributing to cognitive decline that reduces independent function. Pathophysiological pathways are not well defined for neurodegenerative diseases such as dementia; however, published evidence has shown the role of numerous inflammatory processes in the brain contributing toward their pathology. Microglia of the central nervous system (CNS) are the principal components of the brain's immune defence system and can detect harmful or external pathogens. When stimulated, the cells trigger neuroinflammatory responses by releasing proinflammatory chemokines, cytokines, reactive oxygen species, and nitrogen species in order to preserve the cell's microenvironment. These proinflammatory markers include cytokines such as IL-1, IL-6, and TNFα chemokines such as CCR3 and CCL2 and CCR5. Microglial cells may produce a prolonged inflammatory response that, in some circumstances, is indicated in the promotion of neurodegenerative diseases. The present review is focused on the involvement of microglial cell activation throughout neurodegenerative conditions and the link between neuroinflammatory processes and dementia.

Comparing Autoregressive and Network Features for Classification of Depression and Anxiety.

Autocorrelation in functional MRI (fMRI) time series has been studied for decades, mostly considered as noise in the time series which is removed via prewhitening with an autoregressive model. Recent results suggest that the coefficients of an autoregressive model t to fMRI data may provide an indicator of underlying brain activity, suggesting that prewhitening could be removing important diagnostic information. This paper explores the explanatory value of these autoregressive features extracted from fMRI by considering the use of these features in a classification task. As a point of comparison, functional network based features are extracted from the same data and used in the same classification task. We find that in most cases, network based features provide better classification accuracy. However, using principal component analysis to combine network based features and autoregressive features for classification based on a support vector machine provides improved classification accuracy compared to single features or network features, suggesting that when properly combined there may be additional information to be gained from autoregressive features.

Temperature- and Defect-Induced Uniaxial Tensile Mechanical Behaviors and the Fracture Mechanism of Two-Dimensional Silicon Germanide.

Recently, monolayer silicon germanide (SiGe), a newly explored buckled honeycomb configuration of silicon and germanium, is predicted to be a promising nanomaterial for next-generation nanoelectromechanical systems (NEMS) due to its intriguing electronic, optical, and piezoelectric properties. In the NEMS applications, the structure is subjected to uniaxial tensile mechanical loading, and the investigation of the mechanical behaviors is of fundamental importance to ensure structural stability. Here, we systematically explored the uniaxial tensile mechanical properties of 2D-SiGe through molecular dynamics simulations. The effects of temperature ranges from 300 to 1000 K and vacancy defects, for instance, point and bi vacancy, for both armchair and zigzag orientations of 2D-SiGe were investigated. In addition, the influence of system areas and strain rates on the stress-strain performance of 2D-SiGe has also been studied. With the increase in temperature and vacancy concentration, the mechanical properties of 2D-SiGe show decreasing behavior for both orientations and the armchair chirality shows superior mechanical strength to the zigzag direction due to its bonding characteristics. A phase transformation-induced second linearly elastic region was observed at large deformation strain, leading to an anomalous stress-strain behavior in the zigzag direction. At 300 K temperature, we obtained a fracture stress of ∼94.83 GPa and an elastic modulus of ∼388.7 GPa along the armchair direction, which are about ∼3.17 and ∼2.83% higher than the zigzag-oriented fracture strength and elastic modulus. Moreover, because of the strong regularity interruption effect, the point vacancy shows the largest decrease in fracture strength, elastic modulus, and fracture strain compared to the bi vacancy defects for both armchair and zigzag orientations. Area and strain rate investigations reveal that 2D-SiGe is less susceptible to the system area and strain rate. These findings provide a deep insight into controlling the tensile mechanical behavior of 2D-SiGe for its applications in next-generation NEMS and nanodevices.