Transport of synaptic vesicles is modulated by vesicular reversals and stationary cargo clusters.

Stationary clusters of vesicles are a prominent feature of axonal transport, but little is known about their physiological and functional relevance to axonal transport. Here, we investigated the role of vesicle motility characteristics in modulating the formation and lifetimes of such stationary clusters, and their effect on cargo flow. We developed a simulation model describing key features of axonal cargo transport, benchmarking the model against experiments in the posterior lateral mechanosensory neurons of Caenorhabditis elegans. Our simulations included multiple microtubule tracks and varied cargo motion states, and account for dynamic cargo-cargo interactions. Our model also incorporates static obstacles to vesicle transport in the form of microtubule ends, stalled vesicles and stationary mitochondria. We demonstrate, both in simulations and in an experimental system, that a reduction in reversal rates is associated with a higher proportion of long-lived stationary vesicle clusters and reduced net anterograde transport. Our simulations support the view that stationary clusters function as dynamic reservoirs of cargo vesicles, and reversals aid cargo in navigating obstacles and regulate cargo transport by modulating the proportion of stationary vesicle clusters along the neuronal process.

Cross-linkers at growing microtubule ends generate forces that drive actin transport.

SignificanceComplex cellular processes such as cell migration require coordinated remodeling of both the actin and the microtubule cytoskeleton. The two networks for instance exert forces on each other via active motor proteins. Here we show that, surprisingly, coupling via passive cross-linkers can also result in force generation. We specifically study the transport of actin filaments by growing microtubule ends. We show by cell-free reconstitution experiments, computer simulations, and theoretical modeling that this transport is driven by the affinity of the cross-linker for the chemically distinct microtubule tip region. Our work predicts that growing microtubules could potentially rapidly relocate newly nucleated actin filaments to the leading edge of the cell and thus boost migration.

A machine learning approach to predict daptomycin exposure from two concentrations based on Monte Carlo simulations.

Daptomycin is a concentration-dependent lipopeptide antibiotic for which exposure/effect relationships have been shown. Machine learning (ML) algorithms, developed to predict the individual exposure to drugs, have shown very good performances in comparison to maximum a posteriori Bayesian estimation (MAP-BE). The aim of this work was to predict the area under the blood concentration curve (AUC) of daptomycin from two samples and a few covariates using XGBoost ML algorithm trained on Monte Carlo simulations. Five thousand one hundred fifty patients were simulated from two literature population pharmacokinetics models. Data from the first model were split into a training set (75%) and a testing set (25%). Four ML algorithms were built to learn AUC based on daptomycin blood concentration samples at pre-dose and 1 h post-dose. The XGBoost model (best ML algorithm) with the lowest root mean square error (RMSE) in a 10-fold cross-validation experiment was evaluated in both the test set and the simulations from the second population pharmacokinetic model (validation). The ML model based on the two concentrations, the differences between these concentrations, and five other covariates (sex, weight, daptomycin dose, creatinine clearance, and body temperature) yielded very good AUC estimation in the test (relative bias/RMSE = 0.43/7.69%) and validation sets (relative bias/RMSE = 4.61/6.63%). The XGBoost ML model developed allowed accurate estimation of daptomycin AUC using C0, C1h, and a few covariates and could be used for exposure estimation and dose adjustment. This ML approach can facilitate the conduct of future therapeutic drug monitoring (TDM) studies.

Population pharmacokinetics and target attainment analysis of vancomycin after intermittent dosing in adults with cystic fibrosis.

Vancomycin is the first-line agent to treat pulmonary infections caused by methicillin-resistant Staphylococcus aureus (MRSA) in people with cystic fibrosis (PwCF). However, there is no consensus on vancomycin initial dosing in this population among health institutions, and there is a large variability in initial dosing across the United States. In this study, we characterized the pharmacokinetics (PK) of vancomycin in PwCF using a population PK approach. The clinical PK data to develop the population PK model were obtained from vancomycin therapeutic monitoring data from PwCF undergoing treatment for infections due to MRSA. The population PK model was then used to perform comprehensive Monte Carlo simulations to evaluate the probability of target attainment (PTA) of 12 different initial dosing scenarios. The area under the curve to minimum inhibitory concentration (MIC) ratio ≥400 mg*h/L and <650 mg*h/L were used as efficacy and toxicity targets for PTA analysis. A total of 181 vancomycin plasma concentrations were included in the analysis. A one-compartment model with first-order elimination best described the data. Weight significantly influenced the vancomycin PK (P < 0.05). In the final model, clearance was estimated as 5.52 L/h/70 kg, and the volume of distribution was 31.5 L/70 kg. The PTA analysis showed that at MIC = 1 µg/mL, doses 1,500 q8h and 2,000 q12h showed the highest %PTA in achieving both efficacy and toxicity targets. The PTA results from this study may potentially inform the initial dosing regimens of vancomycin to treat pulmonary infections due to MRSA in PwCF.

Population pharmacokinetic rationale for intravenous contezolid acefosamil followed by oral contezolid dosage regimens.

Contezolid is a novel oxazolidinone antibiotic with a promising safety profile. Oral contezolid and its intravenous (IV) prodrug contezolid acefosamil (CZA) are in development for treatment of diabetic foot and acute bacterial skin and skin structure infections (ABSSSI). The prodrug CZA is converted to active contezolid via intermediate MRX-1352. This study aimed to provide the pharmacokinetic rationale for safe, effective, and flexible dosage regimens with initial IV CZA followed by oral contezolid. We simultaneously modeled plasma concentrations from 110 healthy volunteers and 74 phase 2 patients with ABSSSI via population pharmacokinetics (using the importance sampling estimation algorithm), and optimized dosage regimens by Monte Carlo simulations. This included data on MRX-1352, contezolid, and its metabolite MRX-1320 from 66 healthy volunteers receiving intravenous CZA (150-2400 mg) for up to 28 days, and 74 patients receiving oral contezolid [800 mg every 12 h (q12h)] for 10 days. The apparent total clearance for 800 mg oral contezolid with food was 16.0 L/h (23.4% coefficient of variation) in healthy volunteers and 17.7 L/h (53.8%) in patients. CZA was rapidly converted to MRX-1352, which subsequently transformed to contezolid. The proposed dosage regimen used an IV CZA 2000 mg loading dose with 1000 mg IV CZA q12h as maintenance dose(s), followed by 800 mg oral contezolid q12h (with food). During each 24-h period, Monte Carlo simulations predicted this regimen to achieve consistent areas under the curve of 91.9 mg·h/L (range: 76.3-106 mg·h/L) under all scenarios. Thus, this regimen was predicted to reliably achieve efficacious contezolid exposures independent of timing of switch from IV CZA to oral contezolid.IMPORTANCEThis study provides the population pharmacokinetic rationale for the dosage regimen of the intravenous (IV) prodrug contezolid acefosamil (CZA) followed by oral contezolid. We developed the first integrated population model for the pharmacokinetics of the MRX-1352 intermediate prodrug, active contezolid, and its main metabolite MRX-1320 based on data from three clinical studies in healthy volunteers and phase 2 patients. The proposed regimen was predicted to reliably achieve efficacious contezolid exposures independent of timing of switch from IV CZA to oral contezolid.

Signatures of microstructure in gradient-echo and spin-echo signals.

PURPOSE: To determine whether the spatial scale and magnetic susceptibility of microstructure can be evaluated robustly from the decay of gradient-echo and spin-echo signals. THEORY AND METHODS: Gradient-echo and spin-echo images were acquired from suspensions of spherical polystyrene microbeads of 10, 20, and 40 µm nominal diameter. The sizes of the beads and their magnetic susceptibility relative to the medium were estimated from the signal decay curves, using a lookup table generated from Monte Carlo simulations and an analytic model based on the Gaussian phase approximation. RESULTS: Fitting Monte Carlo predictions to spin-echo data yielded acceptable estimates of microstructural parameters for the 20 and 40 µm microbeads. Using gradient-echo data, the Monte Carlo lookup table provided satisfactory parameter estimates for the 20 µm beads but unstable results for the diameter of the largest beads. Neither spin-echo nor gradient-echo data allowed accurate parameter estimation for the smallest beads. The analytic model performed poorly over all bead sizes. CONCLUSIONS: Microstructural sources of magnetic susceptibility produce distinctive non-exponential signatures in the decay of gradient-echo and spin-echo signals. However, inverting the problem to extract microstructural parameters from the signals is nontrivial and, in certain regimes, ill-conditioned. For microstructure with small characteristic length scales, parameter estimation is hampered by the difficulty of acquiring accurate data at very short echo times. For microstructure with large characteristic lengths, the gradient-echo signal approaches the static-dephasing regime, where it becomes insensitive to size. Applicability of the analytic model was further limited by failure of the Gaussian phase approximation for all but the smallest beads.

Effect of particle size on liver MRI R 2 * relaxometry: Monte Carlo simulation and phantom studies.

PURPOSE: To investigate the effect of particle size on liver R 2 * $$ {\mathrm{R}}_2^{\ast } $$ by Monte Carlo simulation and phantom studies at both 1.5 T and 3.0 T. METHODS: Two kinds of particles (i.e., iron sphere and fat droplet) with varying sizes were considered separately in simulation and phantom studies. MRI signals were synthesized and analyzed for predicting R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , based on simulations by incorporating virtual liver model, particle distribution, magnetic field generation, and proton movement into phase accrual. In the phantom study, iron-water and fat-water phantoms were constructed, and each phantom contained 15 separate vials with combinations of five particle concentrations and three particle sizes. R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements in the phantom were made at both 1.5 T and 3.0 T. Finally, differences in R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions or measurements were evaluated across varying particle sizes. RESULTS: In the simulation study, strong linear and positively correlated relationships were observed between R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions and particle concentrations across varying particle sizes and magnetic field strengths ( r ≥ 0.988 $$ r\ge 0.988 $$ ). The relationships were affected by iron sphere size ( p < 0.001 $$ p<0.001 $$ ), where smaller iron sphere size yielded higher predicted R 2 * $$ {\mathrm{R}}_2^{\ast } $$ , whereas fat droplet size had no effect on R 2 * $$ {\mathrm{R}}_2^{\ast } $$ predictions ( p ≥ 0.617 $$ p\ge 0.617 $$ ) for constant total fat concentration. Similarly, the phantom study showed that R 2 * $$ {\mathrm{R}}_2^{\ast } $$ measurements were relatively sensitive to iron sphere size ( p ≤ 0.004 $$ p\le 0.004 $$ ) unlike fat droplet size ( p ≥ 0.223 $$ p\ge 0.223 $$ ). CONCLUSION: Liver R 2 * $$ {\mathrm{R}}_2^{\ast } $$ is affected by iron sphere size, but is relatively unaffected by fat droplet size. These findings may lead to an improved understanding of the underlying mechanisms of R 2 * $$ {\mathrm{R}}_2^{\ast } $$ relaxometry in vivo, and enable improved quantitative MRI phantom design.

Noise-reduction techniques for 1H-FID-MRSI at 14.1 T: Monte Carlo validation and in vivo application.

Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a powerful tool that enables the multidimensional non-invasive mapping of the neurochemical profile at high resolution over the entire brain. The constant demand for higher spatial resolution in 1H-MRSI has led to increased interest in post-processing-based denoising methods aimed at reducing noise variance. The aim of the present study was to implement two noise-reduction techniques, Marchenko-Pastur principal component analysis (MP-PCA) based denoising and low-rank total generalized variation (LR-TGV) reconstruction, and to test their potential with and impact on preclinical 14.1 T fast in vivo 1H-FID-MRSI datasets. Since there is no known ground truth for in vivo metabolite maps, additional evaluations of the performance of both noise-reduction strategies were conducted using Monte Carlo simulations. Results showed that both denoising techniques increased the apparent signal-to-noise ratio (SNR) while preserving noise properties in each spectrum for both in vivo and Monte Carlo datasets. Relative metabolite concentrations were not significantly altered by either method and brain regional differences were preserved in both synthetic and in vivo datasets. Increased precision of metabolite estimates was observed for the two methods, with inconsistencies noted for lower-concentration metabolites. Our study provided a framework for how to evaluate the performance of MP-PCA and LR-TGV methods for preclinical 1H-FID MRSI data at 14.1 T. While gains in apparent SNR and precision were observed, concentration estimations ought to be treated with care, especially for low-concentration metabolites.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach.

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

The effect of number of clusters and magnitude of within-cluster homogeneity in outcomes on the performance of four variance estimators for a marginal multivariable Cox regression model fit to clustered data in the context of observational research.

Researchers often estimate the association between the hazard of a time-to-event outcome and the characteristics of individuals and the clusters in which individuals are nested. Lin and Wei's robust variance estimator is often used with a Cox regression model fit to clustered data. Recently, alternative variance estimators have been proposed: the Fay-Graubard estimator, the Kauermann-Carroll estimator, and the Mancl-DeRouen estimator. Using Monte Carlo simulations, we found that, when fitting a marginal Cox regression model with both individual-level and cluster-level covariates: (i) in the presence of weak to moderate within-cluster homogeneity of outcomes, the Lin-Wei variance estimator can result in estimates of the SE with moderate bias when the number of clusters is fewer than 20-30, while in the presence of strong within-cluster homogeneity, it can result in biased estimation even when the number of clusters is as large as 100; (ii) when the number of clusters was less than approximately 20, the Fay-Graubard variance estimator tended to result in estimates of SE with the lowest bias; (iii) when the number of clusters exceeded approximately 20, the Mancl-DeRouen estimator tended to result in estimated standard errors with the lowest bias; (iv) the Mancl-DeRouen estimator used with a t-distribution tended to result in 95% confidence that had the best performance of the estimators; (v) when the magnitude of within-cluster homogeneity in outcomes was strong or very strong, all methods resulted in confidence intervals with lower than advertised coverage rates even when the number of clusters was very large.

Utilizing nanotechnology to boost the reliability and determine the vertical load capacity of pile assemblies.

Because of their high electrocatalytic activity, sensitivity, selectivity, and long-term stability in electrochemical sensors and biosensors, numerous nanomaterials are being used as suitable electrode materials thanks to developments in nanotechnology. Electrochemical sensors and biosensors are two areas where two-dimensional layered materials (2DLMs) are finding increasing utility due to their unusual structure and physicochemical features. Nanosensors, by their unprecedented sensitivity and minute scale, can probe deeper into the structural integrity of piles, capturing intricacies that traditional tools overlook. These advanced devices detect anomalies, voids, and minute defects in the pile structure with unparalleled granularity. Their effectiveness lies in detection and their capacity to provide real-time feedback on pile health, heralding a shift from reactive to proactive maintenance methodologies. Harvesting data from these nanosensors, data was incorporated into a probabilistic model, executing the reliability index calculations through Monte Carlo simulations. Preliminary outcomes show a commendable enhancement in the predictability of vertical bearing capacity, with the coefficient of variation dwindling by up to 12%. The introduction of nanosensors facilitates instantaneous monitoring and fortifies the long-term stability of pile foundations. This study accentuates the transformative potential of nanosensors in geotechnical engineering.

How drug onset rate and duration of action affect drug forgiveness.

Medication nonadherence is one of the largest problems in healthcare today, particularly for patients undergoing long-term pharmacotherapy. To combat nonadherence, it is often recommended to prescribe so-called "forgiving" drugs, which maintain their effect despite lapses in patient adherence. Nevertheless, drug forgiveness is difficult to quantify and compare between different drugs. In this paper, we construct and analyze a stochastic pharmacokinetic/pharmacodynamic (PK/PD) model to quantify and understand drug forgiveness. The model parameterizes a medication merely by an effective rate of onset of effect when the medication is taken (on-rate) and an effective rate of loss of effect when a dose is missed (off-rate). Patient dosing is modeled by a stochastic process that allows for correlations in missed doses. We analyze this "on/off" model and derive explicit formulas that show how treatment efficacy depends on drug parameters and patient adherence. As a case study, we compare the effects of nonadherence on the efficacy of various antihypertensive medications. Our analysis shows how different drugs can have identical efficacies under perfect adherence, but vastly different efficacies for adherence patterns typical of actual patients. We further demonstrate that complex PK/PD models can indeed be parameterized in terms of effective on-rates and off-rates. Finally, we have created an online app to allow pharmacometricians to explore the implications of our model and analysis.

The effect of vaginal cylinder inhomogeneity on the HDR brachytherapy dose calculations using Monte Carlo simulations.

PURPOSE: To analytically assess the heterogeneity effect of vaginal cylinders (VC) made of high-density plastics on dose calculations, considering the prescription point (surface or 5 mm beyond the surface), and benchmark the accuracy of a commercial model-based dose calculation (MBDC) algorithm using Monte Carlo (MC) simulations. METHODS AND MATERIALS: The GEANT4 MC code was used to simulate a commercial 192 Ir HDR source and VC, with diameters ranging from 20 to 35 mm, inside a virtual water phantom. Standard plans were generated from a commercial treatment planning system [TPS-BrachyVision ACUROS (BV)] optimized for a treatment length of 5 cm through two dose calculation approaches: (1) assuming all the environment as water (i.e., Dw,w-MC & Dw,w-TG43 ) and (2) accounting for the heterogeneity of VC applicators (i.e., Dw,w-App-MC & Dw,w-App-MBDC ). The compared isodose lines, and dose & energy difference maps were extracted for analysis. In addition, the dose difference on the peripheral surface, along the applicator and at middle of treatment length, as well as apical tip was evaluated. RESULTS: The Dw,w-App-MC results indicated that the VC heterogeneity can cause a dose reduction of (up to) % 6.8 on average (for all sizes) on the peripheral surface, translating to 1 mm shrinkage of the isodose lines compared to Dw,w-MC . In addition, the results denoted that BV overestimates the dose on the peripheral surface and apical tip of about 3.7% and 17.9%, respectively, (i.e., Dw,w-App-MBDC vs Dw,w-App-MC ) when prescribing to the surface. However, the difference between the two were negligible at the prescription point when prescribing to 5 mm beyond the surface. CONCLUSION: The VCs' heterogeneity could cause dose reduction when prescribing dose to the surface of the applicator, and hence increases the level of uncertainty. Thus, reviewing the TG43 results, in addition to ACUROS, becomes prudent, when evaluating the surface coverage at the apex.

Experimental and Monte Carlo based dosimetric investigation of a novel 3 mm radiosurgery 3 MV beam using the microSilicon detector.

BACKGROUND: The ZAP-X system is a novel gyroscopic radiosurgical system based on a 3 MV linear accelerator and collimator cones with a diameter between 4 and 25 mm. Advances in imaging modalities to detect small and early-stage pathologies allow for an early and less invasive treatment, where a smaller collimator matching the anatomical target could provide better sparing of surrounding healthy tissue. PURPOSE: A novel 3 mm collimator cone for the ZAP-X was developed. This study aims to investigate the usability of a commercial diode detector (microSilicon) for the dosimetric characterization of this small collimator cone; and to investigate the underlying small field perturbation effects. METHODS: Profile measurements in five depths as well as PDD and output ratio measurements were performed with a microSilicon detector and radiochromic EBT3 films. In addition, comprehensive Monte Carlo simulations were performed to validate the measurement observations and to quantify the perturbation effects of the microSilicon detector in these extremely small field conditions. RESULTS: It is shown that the microSilicon detector enables an accurate dosimetric characterization of the 3 mm beam. The profile parameters, such as the FWHM and 20%-80% penumbra width, agree within 0.1 to 0.2 mm between film and detector measurements. The output ratios agree within the measurement uncertainty between microSilicon detector and films, whereas the comparisons of the PDD results show good agreement with the Monte Carlo simulations. The analysis of the perturbation factors of the microSilicon detector reveals a small field correction factor of approximately 3% for the 3 mm circular beam and a correction factor smaller than 1.5% for field diameters above 3 mm. CONCLUSIONS: It could be shown that the microSilicon detector is well-suitable for the characterization of the new 3 mm circular beam of the ZAP-X system.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial.

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

Propensity Score Analysis With Baseline and Follow-Up Measurements of the Outcome Variable.

A common feature in cohort studies is when there is a baseline measurement of the continuous follow-up or outcome variable. Common examples include baseline measurements of physiological characteristics such as blood pressure or heart rate in studies where the outcome is post-baseline measurement of the same variable. Methods incorporating the propensity score are increasingly being used to estimate the effects of treatments using observational studies. We examined six methods for incorporating the baseline value of the follow-up variable when using propensity score matching or weighting. These methods differed according to whether the baseline value of the follow-up variable was included or excluded from the propensity score model, whether subsequent regression adjustment was conducted in the matched or weighted sample to adjust for the baseline value of the follow-up variable, and whether the analysis estimated the effect of treatment on the follow-up variable or on the change from baseline. We used Monte Carlo simulations with 750 scenarios. While no analytic method had uniformly superior performance, we provide the following recommendations: first, when using weighting and the ATE is the target estimand, use an augmented inverse probability weighted estimator or include the baseline value of the follow-up variable in the propensity score model and subsequently adjust for the baseline value of the follow-up variable in a regression model. Second, when the ATT is the target estimand, regardless of whether using weighting or matching, analyze change from baseline using a propensity score that excludes the baseline value of the follow-up variable.

Ionization Detail Parameters for DNA Damage Evaluation in Charged Particle Radiotherapy: Simulation Study Based on Cell Survival Database.

Details of excitation and ionization acts hide a description of the biological effects of charged particle traversal through living tissue. Nanodosimetry enables the introduction of novel quantities that characterize and quantify the particle track structure while also serving as a foundation for assessing biological effects based on this quantification. This presents an opportunity to enhance the planning of charged particle radiotherapy by taking into account the ionization detail. This work uses Monte Carlo simulations with Geant4-DNA code for a wide variety of charged particles and their radiation qualities to analyze the distribution of ionization cluster sizes within nanometer-scale volumes, similar to DNA diameter. By correlating these results with biological parameters extracted from the PIDE database for the V79 cell line, a novel parameter R2 based on ionization details is proposed for the evaluation of radiation quality in terms of biological consequences, i.e., radiobiological cross section for inactivation. By incorporating the probability p of sub-lethal damage caused by a single ionization, we address limitations associated with the usually proposed nanodosimetric parameter Fk for characterizing the biological effects of radiation. We show that the new parameter R2 correlates well with radiobiological data and can be used to predict biological outcomes.

Molecular Insights into Adhesion at Interface of Geopolymer Binder and Cement Mortar.

The degradation of concrete and reinforced concrete structures is a significant technical and economic challenge, requiring continuous repair and rehabilitation throughout their service life. Geopolymers (GPs), known for their high mechanical strength, low shrinkage, and durability, are being increasingly considered as alternatives to traditional repair materials. However, there is currently a lack of understanding regarding the interface bond properties between new geopolymer layers and old concrete substrates. In this paper, using advanced computational techniques, including quantum mechanical calculations and stochastic modeling, we explored the adsorption behavior and interaction mechanism of aluminosilicate oligomers with different Si/Al ratios forming the geopolymer gel structure and calcium silicate hydrate as the substrate at the interface bond region. We analyzed the electron density distributions of the highest occupied and lowest unoccupied molecular orbitals, examined the reactivity indices based on electron density functional theory, performed Mulliken charge population analysis, and evaluated global reactivity descriptors for the considered oligomers. The results elucidate the mechanisms of local and global reactivity of the oligomers, the equilibrium low-energy configurations of the oligomer structures adsorbed on the surface of C-(A)-S-H(I) (100), and their adsorption energies. These findings contribute to a better understanding of the adhesion properties of geopolymers and their potential as effective repair materials.

Quantifying future carbon emissions uncertainties under stochastic modeling and Monte Carlo simulation: Insights for environmental policy consideration for the Belt and Road Initiative Region.

This study critically examines future carbon (CO2) emissions in the Belt & Road Initiative (BRI) region, considering factors such as energy consumption, economic growth, population growth, and population density. The objective of this study is to identify critical areas of higher emissions, which require policy intervention capable of strengthening sustainability in the BRI compact. A combined approach of stochastic modeling and Monte Carlo simulations was employed, utilizing panel data from 45 countries in the BRI region from 1990 to 2021. Results confirm that emissions are higher in all scenarios in direct proportion to electric power consumption, population growth, and Gross Domestic Product (GDP) growth. In scenarios with high emissions, a continuous and significant upward trend in CO2 emissions was observe. The medium emissions scenario exhibited a more moderated rise in emissions, suggesting a balance between economic development and environmental considerations. Critical areas for future environmental policy-making resides in electric power consumption, population growth, and GDP growth. The study strongly recommends for a shift from the current focus on road and railway infrastructure to renewable energy infrastructure, green innovations and efficient technology transfer to member countries. Without this, the BRI region is likely to face increased emissions, posing significant challenges to future sustainable development and global environmental sustainability.

Predicting Organometallic Intermediates in the Surface-Assisted Ullmann Coupling of Chrysene Isomers.

On-surface polymerization of functional organic molecules has been recently recognized as a promising route to persistent low-dimensional structures with tailorable properties. In this contribution, using the coarse-grained Monte Carlo simulation method, we study the initial stage of the Ullmann coupling of doubly halogenated chrysene isomers adsorbed on a catalytically active (111) crystalline surface. To that end, we focus on the formation of labile metal-organic precursor structures preceding the covalent bonding of chrysene monomers. Four monomeric chrysene units with differently distributed halogen substituents were probed in the simulations, and the resulting precursor structures were compared and quantified. Moreover, the effect of (pro)chirality of chrysene tectons on the structure formation was elucidated by running separate simulations in enantiopure and racemic systems. The calculations showed that suitable manipulation of the halogen substitution pattern allows for the creation of diverse precursor architectures, ranging from straight and winded chains to cyclic oligomers with enantiopure, racemic, and nonracemic composition. The obtained findings can be helpful in developing synthetic strategies for covalent polymers with predefined architecture and functionality.