Duality for Koszul homology over Gorenstein rings

We study Koszul homology over local Gorenstein rings. It is well known that if an ideal is strongly Cohen–Macaulay the Koszul homology algebra satisfies Poincaré duality. We prove a version of this duality which holds for all ideals and allows us to give two criteria for an ideal to be strongly Cohen–Macaulay. The first can be compared to a result of Hartshorne and Ogus; the second is a generalization of a result of Herzog, Simis, and Vasconcelos using sliding depth.     

Smart and green interfaces: From single bubbles/drops to industrial environmental and biomedical applications

Interfaces can be called Smart and Green (S&G) when tailored such that the required technologies can be implemented with high efficiency, adaptability and selectivity. At the same time they also have to be eco-friendly, i.e. products must be biodegradable, reusable or simply more durable. Bubble and drop interfaces are in many of these smart technologies the fundamental entities and help develop smart products of the everyday life.     

Operando X-ray absorption and EPR evidence for a single electron redox process in copper catalysis

An unprecedented single electron redox process in copper catalysis is confirmed using operando X-ray absorption and EPR spectroscopies. The oxidation state of the copper species in the interaction between Cu(ii) and a sulfinic acid at room temperature, and the accurate characterization of the formed Cu(i) are clearly shown using operando X-ray absorption and EPR evidence. Further investigation of anion effects on Cu(ii) discloses that bromine ions can dramatically increase the rate of the redox process. Moreover, it is proven that the sulfinic acids are converted into sulfonyl radicals, which can be trapped by 2-arylacrylic acids and various valuable β-keto sulfones are synthesized with good to excellent yields under mild conditions.     

ThrEEBoost: Thresholded Boosting for Variable Selection and Prediction via Estimating Equations

Most variable selection techniques for high-dimensional models are designed to be used in settings, where observations are independent and completely observed. At the same time, there is a rich literature on approaches to estimation of low-dimensional parameters in the presence of correlation, missingness, measurement error, selection bias, and other characteristics of real data. In this article, we present ThrEEBoost (Thresholded EEBoost), a general-purpose variable selection technique which can accommodate such problem characteristics by replacing the gradient of the loss by an estimating function. ThrEEBoost generalizes the previously proposed EEBoost algorithm (Wolfson 2011 Wolfson, J. (2011), “EEBoost: A General Method for Prediction and Variable Selection Based on Estimating Equations,” Journal of the American Statistical Association, 106, 296–305.[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) by allowing the number of regression coefficients updated at each step to be controlled by a thresholding parameter. Different thresholding parameter values yield different variable selection paths, greatly diversifying the set of models that can be explored; the optimal degree of thresholding can be chosen by cross-validation. ThrEEBoost was evaluated using simulation studies to assess the effects of different threshold values on prediction error, sensitivity, specificity, and the number of iterations to identify minimum prediction error under both sparse and nonsparse true models with correlated continuous outcomes. We show that when the true model is sparse, ThrEEBoost achieves similar prediction error to EEBoost while requiring fewer iterations to locate the set of coefficients yielding the minimum error. When the true model is less sparse, ThrEEBoost has lower prediction error than EEBoost and also finds the point yielding the minimum error more quickly. The technique is illustrated by applying it to the problem of identifying predictors of weight change in a longitudinal nutrition study. Supplementary materials are available online.     

Fiber Integrated Metal-Organic Frameworks as Functional Components in Smart Textiles

Owing to high modularity and synthetic tunability, metal–organic frameworks (MOFs) on textiles are poised to contribute to the development of state-of-the-art wearable systems with multifunctional performance. While these composite materials have demonstrated promising functions in sensing, filtration, detoxification, and biomedicine, their applicability in multifunctional systems is only beginning to materialize. This review highlights the multifunctionality and versatility of MOF-integrated textile systems. It summarizes the operational goals of MOF@textile composites, encompassing sensing, filtration, detoxification, drug delivery, UV protection, and photocatalysis. Building upon these recent advances, this review concludes with an outlook on emerging opportunities for the diverse applications of MOF@textile systems in the realm of smart wearables.     

Single Chlamydomonas reinhardtii cell separation from bacterial cells and auto-fluorescence tracking with a nanosieve device

A planar, transparent, and adaptable nanosieve device is developed for efficient microalgae/bacteria separation. In the proposed method, a sacrificial layer is applied with dual photolithography patterning to achieve a 1D channel with a very low aspect ratio (1:10 000). A microalgae/bacteria mixture is then introduced into the deformable PDMS nanochannel. The hydrodynamic deformation of the nanochannel is regulated to allow the bacteria cells to pass through while leaving the microalgae cells trapped in the device. At a flow rate of 4 μL/min, the supernatant collected from the device is indistinguishable from a control solution, indicating that nearly all the microalgae cells are trapped in the device. Additionally, this device is capable of single cell auto-fluorescence tracking. These microalgae cells demonstrate minimal photobleaching over 250 s laser exposure and could be used to monitor hazardous compounds in the sample with a continuous flow. This method will be valuable to purify microalgae samples containing contaminations and study single-cell heterogeneity.     

Relevance of gaseous flows in electrochemically assisted soil thermal remediation

Treatment of polluted soil is one of the priorities in the search of a more sustainable planet. Electrochemically assisted soil remediation has been considered a good option for removing organic contaminants contained in soil, including the removal of volatile organic compounds, associated with gaseous streams produced during the treatment. Also, recently, electrochemical gas treatment technologies have been appointed as promising for the treatment of volatile organic compounds. In this work, we review the current opinion about the most recent studies in both areas. The first section focuses on the production of gaseous compounds during soil remediation by conventional and electrochemical systems. The second section describes the recent progress in the integration of adsorption and absorption with electrochemical processes. Finally, we discuss the holistic application of assisted electrochemical technologies in soil remediation, considering also emerging processes recently published in the literature.