Greenhouse gas emissions of bio-based diapers containing chemically modified protein superabsorbents

Replacing the current mainly fossil-based, disposable, and non-biodegradable sanitary products with sustainable, functional alternatives is an industry priority. Suggested biobased alternatives require evaluation of their actual impact on greenhouse gas (GHG) emissions. We evaluated GHG emissions of biobased baby diapers as the most consumed sanitary product, using a biodegradable functionalized protein superabsorbent polymer (bioSAP) and compared them with currently used fossil-based counterparts. Assessment of the diapers also included estimated GHG emissions from the production of the biobased components, transport, and end-of-life combustion of these items. It was shown that only a few of the biobased diaper alternatives resulted in lower GHG emissions than commercial diapers containing fossil-based materials. At the same time, it was demonstrated that the production of the bioSAP via chemical modification of a protein raw material is the primary GHG contributor, with 78% of the total emissions. Reduction of the GHG contribution of the bioSAP production was achieved via a proposed recycling route of the functionalization agent, reducing the GHG emissions by 13% than if no recycling was carried out. Overall, we demonstrated that reduced and competitive GHG emissions could be achieved in sanitary articles using biobased materials, thereby contributing to a sanitary industry producing disposable products with less environmental pollution while allowing customers to keep their current consumption patterns

High Capacity Functionalized Protein Superabsorbents from an Agricultural Co-Product: A Cradle-to-Cradle Approach

Synthesis of superabsorbent particles from nontoxic wheat gluten (WG) protein, as an industrial co-product, is presented. A natural molecular cross-linker named genipin (a hydrogenated glycoside) is used together with a dianhydride (ethylenediaminetetraacetic EDTAD), to enable the preparation of a material with a network structure capable of swelling up to approximate to 4000% in water and approximate to 600% in saline solution. This represents an increase in swelling by over 10 times compared to the already highly absorbing gluten reference material. The carboxylation (using EDTAD) and the cross-linking of the protein result in a hydrogel with liquid retention capacity as high as 80% of the absorbed water remaining in the WG network on extensive centrifugation, which is higher than that of commercial fossil-based superabsorbents. The results also show that more polar forms of the reacted genipin are more effectively grafted onto the protein, contributing to the swelling and liquid retention. Microscopy of the materials reveals extensive nanoporosity (300 nm), contributing to rapid capillarity-driven absorption. The use of proteins from agricultural industries for the fabrication of sustainable protein superabsorbents is herein described as an emerging avenue for the development of the next generation daily-care products with a minimal environmental impact

Co-immobilization multienzyme nanoreactor with co-factor regeneration for conversion of CO2

Multienzymatic conversion of carbon dioxide (CO2) into chemicals has been extensively studied. However, regeneration and reuse of co-factor are still the main problems for the efficient conversion of CO2. In this study, a nanoscale multienzyme reactor was constructed by encapsulating simultaneously carbonic anhydrase (CA), formate dehydrogenase (FateDH), co-factor (NADH), and glutamate dehydrogenases (GDH) into ZIF-8. In the multienzyme reactors, cationic polyelectrolyte (polyethyleneimine, PEI) was doped in the ZIF-8 by dissolving it in the precursors of ZIF-8. Co-factor (NADH) was anchored in ZIF-8 by ion exchange between PEI (positive charge) and co-factor (negative charge), and regenerated through GDH embedded in the ZIF-8, thus keeping high activity of FateDH. Activity recovery of FateDH in the multienzyme reactors reached 50%. Furthermore, the dissolution of CO2 in the reaction solution was increased significantly by the combination of CA and ZIF-8. As a result, the nanoscale multienzyme reactor exhibited superior capacity for conversion of CO2 to formate. Compared with free multienzyme system, formate yield was increased 4.6-fold by using the nanoscale multienzyme reactor. Furthermore, the nanoscale multienzyme reactor still retained 50% of its original productivity after 8 cycles, indicating excellent reusability

Como devemos pensar sobre a prosperidade comum e seus desafios no contexto da capitalização?

China is actively promoting common prosperity to address the contradiction of unbalanced and inadequate development. Financialization has become the backdrop for common prosperity. How understanding the changing connotations of common prosperity and the factors influencing it in this context becomes the subject of this paper. We argue that the imbalance between income from assets and labor and the new forms of value generation are the reasons why common prosperity is difficult to achieve. To justify this conclusion, this paper examines financialization from the critical perspective of the Marxist political economy, cites the financialization case in China and discusses the real and ideological challenges facing common prosperity. The paper analyzes the nature of a new form of fetishism, financialization fetishism, and introduces the concept of narrative value, thus exposing the distortion of people's value ideology by financialization fetishism and the obscuring and erosion of labor value by the mystification of narrative value.A China promove ativamente a prosperidade comum e resolve a contradição do desequilíbrio do desenvolvimento. A capitalização tornou-se o pano de fundo da prosperidade comum. Como entender as mudanças na conotação da prosperidade comum e seus fatores de influência, no contexto da capitalização, tornou-se o tema deste artigo. Acredita-se que o desequilíbrio entre ativos e renda do trabalho e a nova forma de geração de valor são as razões pelas quais a prosperidade comum é difícil de alcançar. A fim de provar a racionalidade dessa conclusão, este artigo examina a questão da capitalização a partir da perspectiva crítica da economia política marxista e toma o caso de capitalização da China como um exemplo para explorar os desafios práticos e os desafios ideológicos enfrentados pela prosperidade comum. Este texto analisa uma nova forma de fetichismo - a essência do fetichismo financeiro introduz o conceito de valor narrativo e revela a distorção e a erosão do valor do fetichismo financeiro sobre os valores das pessoas e o mistério do valor narrativo

SAOCNN: Self-Attention and One-Class Neural Networks for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is a popular research direction for hyperspectral images; however, it is problematic because it separates the background and anomaly without prior target information. Currently, deep neural networks are used as an extractor to mine intrinsic features in hyperspectral images, which can be fed into separate anomaly detection methods to improve their performances. However, this hybrid approach is suboptimal because the subsequent detector is unable to drive the data representation in hidden layers, which makes it a challenge to maximize the capabilities of deep neural networks when extracting the underlying features customized for anomaly detection. To address this issue, a novel unsupervised, self-attention-based, one-class neural network (SAOCNN) is proposed in this paper. SAOCNN consists of two components: a novel feature extraction network and a one-class SVM (OC-SVM) anomaly detection method, which are interconnected and jointly trained by the OC-SVM-like loss function. The adoption of co-training updates the feature extraction network together with the anomaly detector, thus improving the whole network’s detection performance. Considering that the prominent feature of an anomaly lies in its difference from the background, we designed a deep neural extraction network to learn more comprehensive hyperspectral image features, including spectral, global correlation, and local spatial features. To accomplish this goal, we adopted an adversarial autoencoder to produce the residual image with highlighted anomaly targets and a suppressed background, which is input into an improved non-local module to adaptively select the useful global information in the whole deep feature space. In addition, we incorporated a two-layer convolutional network to obtain local features. SAOCNN maps the original hyperspectral data to a learned feature space with better anomaly separation from the background, making it possible for the hyperplane to separate them. Our experiments on six public hyperspectral datasets demonstrate the state-of-the-art performance and superiority of our proposed SAOCNN when extracting deep potential features, which are more conducive to anomaly detection

SAOCNN: Self-Attention and One-Class Neural Networks for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is a popular research direction for hyperspectral images; however, it is problematic because it separates the background and anomaly without prior target information. Currently, deep neural networks are used as an extractor to mine intrinsic features in hyperspectral images, which can be fed into separate anomaly detection methods to improve their performances. However, this hybrid approach is suboptimal because the subsequent detector is unable to drive the data representation in hidden layers, which makes it a challenge to maximize the capabilities of deep neural networks when extracting the underlying features customized for anomaly detection. To address this issue, a novel unsupervised, self-attention-based, one-class neural network (SAOCNN) is proposed in this paper. SAOCNN consists of two components: a novel feature extraction network and a one-class SVM (OC-SVM) anomaly detection method, which are interconnected and jointly trained by the OC-SVM-like loss function. The adoption of co-training updates the feature extraction network together with the anomaly detector, thus improving the whole network’s detection performance. Considering that the prominent feature of an anomaly lies in its difference from the background, we designed a deep neural extraction network to learn more comprehensive hyperspectral image features, including spectral, global correlation, and local spatial features. To accomplish this goal, we adopted an adversarial autoencoder to produce the residual image with highlighted anomaly targets and a suppressed background, which is input into an improved non-local module to adaptively select the useful global information in the whole deep feature space. In addition, we incorporated a two-layer convolutional network to obtain local features. SAOCNN maps the original hyperspectral data to a learned feature space with better anomaly separation from the background, making it possible for the hyperplane to separate them. Our experiments on six public hyperspectral datasets demonstrate the state-of-the-art performance and superiority of our proposed SAOCNN when extracting deep potential features, which are more conducive to anomaly detection

Nanocrystal Synthesis with Alkoxy Ligands and Solvents

Applications of colloidal nanocrystals in polar solvents often require nanocrystals synthesized in nonpolar solvents. However, solvent transfer processes are problematic and deteriorate nanocrystal quality. Here we report syntheses of nanocrystals with nearly universal solvent dispersibility using ligands and solvents with alkoxy repeating units. Core syntheses shell deposition, and cation exchange proceed similarly to traditional methods while products are more stable in aqueous solution than those generated by solvent transfer. (CdSe)CdZnS nanocrystals retain photoluminescence in cells for single-particle tracking experiments and outperform other nanocrystal classes in diffusion metrics reflecting stability and nonspecific binding. Distinct reaction classes yield nanocrystals with either methoxy or hydroxy ligand terminations, both of which can be purified by aqueous methods that are greener than traditional methods. These reactions can further generate nanocrystals with diverse compositions (oxides, sulfides, and selenides), shapes, and spectral bands with wide dispersibility that may make applications in polar solvents more widely accessible