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Combining β-CD with sulfonic surfactants yielded a novel nanotubular architecture which enhanced the rheological properties in aqueous phases.

Recent studies have shown that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their viscoelastic properties, it is important to understand how the nanostructures change under different solution conditions. However, it is challenging to obtain a three-dimensional molecular description of these nanostructures by utilizing conventional experimental characterization techniques or thermodynamic models. To this end, we combined the structural data from small angle X-ray scattering (SAXS) experiments and thermodynamic knowledge from coarse-grained Monte Carlo (CGMC) simulations to identify the detailed three-dimensional nanostructure of DBCs. Specifically, unbiased CGMC simulations are performed with SAXS-guided initial conditions, which aids us to sample accurate nanostructures in a computationally efficient fashion. As a result, an elliptical bilayer nanostructure is obtained as the most probable nanostructure of DBCs whose dimensions are validated by scanning electron microscope (SEM) images. Then, utilizing the obtained molecular model of DBCs, we could also explain the pH tunability of the system. Overall, our results from SAXS-guided unbiased CGMC simulations highlight that using potential energy combined with SAXS data, we can distinguish otherwise degenerate nanostructures resulting from the inherent ambiguity of SAXS patterns.

Glycans are the most abundant and diverse fundamental biomolecules. Profiling glycans is essential to elucidate biomolecular mechanisms and to predict biological consequences; however, the analysis of glycans remains challenging due to their structural complexity. A novel glycan detection platform is established through the integration of surface-enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. SERS is able to distinguish isomeric structures and provide fingerprint information of molecules. Boronic acid receptors can selectively bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. For proof-of-concept, two boronic acids, 4-mercaptophenylboronic acid (4MBA) and 1-thianthrenylboronic acid (1TBA) were used for glycan detection. The spectra were analyzed by the machine learning algorithm. The sensing platform successfully recognized the stereoisomers (glucose, mannose, and galactose), the structural isomers with α-1,4 and α-1,6 glycosidic linkages, and the β-1,4 glycosidic linkage within lactose molecules. The collective spectra that combine the spectra from both boronic acid receptors could improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition to qualitative analysis, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. The quantification accuracy reached a coefficient of determination (R2) value of 0.998 and a normalized mean square error (NMSE) of 0.00195. This low-cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent glycan screening in standard biological laboratories.

Flexible solid-state Zn–air batteries with CoS1−δ/SnS2−δ and CBCs demonstrate a high energy density of 256 Wh kgcell−1 including all active and inactive components, high operating rates of 50 to 100 mA cm−2, and a temperature range of +80 to −40 °C.

The largest impact of land-use change on catchment hydrology can be linked to deforestation. This change, driven by exponential population growth, intensified food and industrial production, has resulted in alterations in river flow regimes such as high peaks, reduced base flows, and silt deposition. To reverse this trend more extensive management practices are becoming increasingly important, but can also lead to severe losses in agricultural production. Land-use optimization tools can help catchment managers to explore numerous land-use configurations for the evaluation of trade-offs amongst various uses. In this study, the Soil and water assessment tool (SWAT) model was coupled with a genetic algorithm to identify land-use/management configurations with minimal trade-offs between environmental objectives (reduced sediment load, increased stream low flow) and the crop yields of maize and soybean in Nyangores catchment (Kenya). During the land-use optimization, areas under conventional agriculture could either remain as they are or change to agroforestry or conservation agriculture (CA), where the latter was represented by introducing contour farming and vegetative filter strips. From the sets of the resulting Pareto-optimal solutions we selected mid-range solutions, representing a fair compromise among all objectives, for further analysis. We found that a combined measure implementation strategy (agroforestry on certain sites and conservation agriculture on other sites within the catchment) proved to be superior over single measure implementation strategies. On the catchment scale, a 3.6% change to forests combined with a 35% change to CA resulted in highly reduced sediment loads (−78%), increased low flow (+14%) and only slightly decreased crop yields (

With the increasing process complexities, data-driven fault prognosis has emerged as a promising fault management tool that predicts and manages abnormal events well in advance. In this paper, we develop a fault prognosis framework named ‘OASIS-P’ by integrating operable adaptive sparse identification of systems (OASIS), which is a data-driven adaptive modeling technique, with a risk-based process monitoring approach and contribution plots. Firstly, OASIS is employed with the risk assessment procedure for the prediction of impending faults. As the OASIS model is adaptive, it copes with the initial fault symptoms and forecasts the future behavior of the process under faulty conditions reasonably well, thereby providing an early fault prediction. Next, the fault isolation step is immediately initiated using contribution plots to identify the faulty variables. Unlike in fault diagnosis, the problem of ambiguity in interpreting contribution results due to fault propagation is not an issue in fault prognosis, if the fault isolation step is implemented at an early stage of the fault before it affects the other variables. Hence, the contribution plots together with OASIS can proactively monitor the process in real-time. As a case study, we demonstrate OASIS-P for fault prognosis of a reactor–separator system.

Wormlike micelles (WLMs), which are the self-assemblies of amphiphilic surfactants, have gained attention in various applications due to their unique ‘living’ nature (i.e., union-scission). However, the industrial production of the WLMs has rarely been discussed. Moreover, given that the system hardly reaches the desired viscosity without appropriate control actions within a reasonable operation time, a model-based controller has been implemented to the production of WLMs where CTAB/NaCl in a semi-batch reactor is used as a case study. For this, a high-fidelity model was developed by integrating thermodynamic, kinetic, and rheology models and validated with experimental results. Subsequently, to alleviate the computational burden of such a high-fidelity model, the model reduction was carried out by the multivariable output error state space algorithm. The developed reduced-order model (ROM) was able to reproduce the essential features of the high-fidelity model and utilized in the formulated model predictive control (MPC) system. Herein, the manipulated variables (i.e., surfactant volume fraction, salt concentration, and temperature) are adjusted to give the desired WLM length of 3700 nm (i.e., a zero-shear viscosity of 100 Pa-s). Finally, the developed MPC scheme resulted in optimal input sequences to successfully reach the setpoint within practical constraints.

Computational fluid dynamics (CFD) models have been widely used in the chemical process industry to analyze various aspects of high-consequence rare events. However, CFD models are computationally intensive in nature, and therefore, several developments have been made in building computationally efficient models. The existing computationally efficient models in the field of high-consequence rare events are temporally static in nature and do not represent system evolution with time, which is crucial for consequence modeling of rare events. Further, the consequences depend on various parameters in addition to inputs. Since it is not affordable to construct a new model for every parameter value, incorporation of inputs and parameters is an additional challenge in developing a consequence model. Hence, to address these challenges, this work proposes a k-nearest neighbor (kNN)-based parametric reduced-order model (PROM) for consequence estimation of rare events to enhance numerical robustness with respect to parameter change. Specifically, local (with respect to parameters) ROMs are constructed based on multivariable output-error state space (MOESP) algorithm for a range of parameters, and they are linearly interpolated to estimate consequences at a new parameter value. The effectiveness of the proposed model is demonstrated through a case study of supercritical carbon dioxide release rare event.

Even though continuous pulp processes have been studied for many years, the absence of a model that can accurately describe the evolution of fiber morphology has impeded the application of advanced...

Percutaneous coronary intervention (PCI) is an uncommon procedure in the pediatric population, and the use of intravascular ultrasound (IVUS) to guide stent optimization in this setting is not well described. We present a case of a 22-month old female who developed left main coronary stenosis following surgical correction of anomalous left coronary artery from the pulmonary artery (ALCAPA). PCI was pursued, and with the use of IVUS, stent placement was determined to be optimal. Given the limited data on the use of IVUS in PCI for children, we discuss the unique applications of IVUS, drug-eluting stents, and future clinical concerns following management of post-surgical stenosis following ALCAPA repair. 〈Learning objective: Percutaneous coronary intervention is a rare procedure in the pediatric population, and the use of intravascular ultrasound to optimize stent placement is not well described in this setting. Intravascular ultrasound may allow for more precise stent placement and may be useful during acute and long-term management of pediatric patients undergoing percutaneous coronary intervention.〉.

This work explores the application of the recently developed Koopman operator approach for model identification and feedback control of a hydraulic fracturing process. Controlling fracture propagation and proppant transport with precision is a challenge due in large part to the difficulty of constructing approximate models that accurately capture the characteristic moving boundary and highly-coupled dynamics exhibited by the process. Koopman operator theory is particularly attractive here as it offers a way to explicitly construct linear representations for even highly nonlinear dynamics. The method is data-driven and relies on lifting the states to an infinite-dimensional space of functions called observables where the dynamics are governed by a linear Koopman operator. This work considers two problems: (a) fracture geometry control, and (b) proppant concentration control. In both cases, an approximate linear model of the corresponding dynamics is constructed and used to design a model predictive controller (MPC). The manuscript shows that in the case of highly nonlinear dynamics, as observed in the proppant concentration, use of canonical functions in the observable basis fails. In such cases, a priori system knowledge can be leveraged to choose the required basis. The numerical experiments demonstrate that the Koopman linear model shows excellent agreement with the real system and successfully achieves the desired target values maximizing the oil and gas productivity. Additionally, due to its linear structure, the Koopman models allow convex MPC formulations that avoid any issues associated with nonlinear optimization.

Root cause analysis of rare but catastrophic events in chemical process industry has to deal with challenges of data scarcity that may lead to inaccurate diagnosis. Previously, Bayesian models (BM)...

Many of the existing offline system identification methods cannot completely comprehend the dynamics of an evolving complex process without relying on impractically large data sets. As a solution to this, a systematic procedure capable of identifying and predicting the nonlinear dynamics on the fly promises to provide a useful representation of the process model. Motivated by this, an adaptive model identification framework that relies on the methods of sparse regression and feature selection is presented in this work. The proposed method is a three-step procedure: (1) identifying potential functions from a candidate library using recently developed Sparse Identification of Nonlinear Dynamics (SINDy), (2) updating coefficients of the identified model using ordinary least-squares regression, (3) selecting the most important features using stepwise regression. The proposed algorithm is implemented as follows. Initially, a baseline model is identified offline using SINDy, and as a new data becomes available, the subsequent online steps are triggered based on a pre-specified tolerance to further update the model. Such an adaptive identification scheme facilitates in perceiving the model structure using a less amount of data than its offline counterpart, SINDy. To highlight its significance, the dynamics of a continuous stirred tank reactor is identified using the proposed adaptive method and is compared with a model identified using SINDy alone.

ImportanceThe antigen-based rapid diagnostic test (Ag-RDT), using saliva specimens, is fast, non-invasive and suitable for SARS-CoV-2 self-testing, unlike nasopharyngeal swab (NPS) testing.ObjectiveTo assess the diagnostic sensitivity of a novel Beanguard gargle™ (BG)-based virus detection method for early diagnosis of COVID-19.DesignThis clinical trial was conducted at Gunsan Medical Center, Namwon Medical Center, and Jeonbuk National University Hospital, between May 7 and July 7, 2021.SettingPaired NPS and BG-based saliva specimens collected from COVID-19 patients and healthy individuals were analyzed using NPS-RT-PCR, BG-RT-PCR, and BG-Ag-RDTs.ParticipantsThe study comprised 102 COVID-19-positive patients hospitalized after governmental screening process and 100 healthy individuals. Forty-five COVID-19 patients were sampled within 6 days of illness and 57 within 7–15 days; 27 were categorized as asymptomatic and 75, as symptomatic. Eight and 2 patients carried the SARS-CoV-2 Alpha and Delta variants, respectively.InterventionThe diagnostic performances of BG-Ag-RDT, BG-RT-PCR, and NPS-RT-PCR for detecting SARS-CoV-2 were compared.Main outcomesThe sensitivities of BG-Ag-RDT and BG-RT-PCR towards salivary viral detection were highly concordant, with no discrimination between symptomatic, asymptomatic, or SARS-CoV-2 variant cases.ResultsAmong total participants (mean age, 43.7 years), 51% were women. BG-Ag-RDTs showed high sensitivity (97.8%, [95% CI, 88.4% to 99.6%]) and specificity (100%, [95% CI, 96.3% to 100%) in 45 patients within 6 days of illness and could detect all cases of SARS-CoV-2 Alpha and Delta variants. In 11 asymptomatic early-stage cases, both BG-Ag-RDTs and BG-RT-PCR showed excellent sensitivity and specificity of 100% (95% CI, 74.1% to 100% and 95% CI, 20.7% to 100%, respectively). The interaction between SARS-CoV-2 spike proteins and truncated canavalin, an active ingredient from bean extract (BE) and the ultrastructural features of SARS-CoV-2 particles coated with BE were observed. The detachment of the SARS-CoV-2 receptor-binding domain from hACE2 increased as the BE concentration increased, allowing the release of the virus from hACE2 for early diagnosis.Conclusions and RelevanceUsing BG-based saliva remarkably enhances the Ag-RDT diagnostic performance as an alternative to NPS and enables rapid and accurate COVID-19 self-testing and mass screening, supporting efficient COVID-19 management.Trial RegistrationKCT0006438Key PointsQuestionHow can we collect SARS-CoV-2 from oral cavity to improve the sensitivity of antigen-based rapid diagnostic test (Ag-RDT)?FindingsIn this clinical study involving 102 hospitalized COVID-19 patients, the Ag-RDT test using Beanguard gargle™-based saliva specimens showed significantly enhanced sensitivity and specificity towards detection of SARS-CoV-2 along with Alpha and Delta variants in all patients tested within 6 days of illness.MeaningOur self-testing method represents an attractive alternative to nasopharyngeal swab RT-PCR for the early diagnosis of symptomatic and asymptomatic COVID-19 cases.

Hydraulic fracturing of unconventional reservoirs has seen a boom in the last century, as a means to fulfill the growing energy demand in the world. The fracturing fluid used in the process plays a substantial role in determining the results. Hence, several research and development efforts have been geared towards developing more sustainable, efficient, and improved fracturing fluids. Herein, we present a dynamic binary complex (DBC) solution, with potential to be useful in the hydraulic fracturing domain. It has a supramolecular structure formed by the self-assembly of low molecular weight viscosifiers (LMWVs) oleic acid and diethylenetriamine into an elongated entangled network under alkaline conditions. With less than 2 wt% constituents dispersed in aqueous solution, a viscous gel that exhibits high viscosities even under shear was formed. Key features include responsiveness to pH and salinity, and a zero-shear viscosity that could be tuned by a factor of ~280 by changing the pH. Furthermore, its viscous properties were more pronounced in the presence of salt. Sand settling tests revealed its potential to hold up sand particles for extended periods of time. In conclusion, this DBC solution system has potential to be utilized as a smart salt-responsive, pH-switchable hydraulic fracturing fluid.