Pub Date : 2023-01-04DOI: 10.3389/fceng.2022.1044245
V. Nold, L. Junghans, B. Bayer, L. Bisgen, M. Duerkop, R. Drerup, B. Presser, T. Schwab, E. Bluhmki, S. Wieschalka, B. Knapp
Introduction: For the implementation of robust bioprocesses, understanding of temporal cell behavior with respect to relevant inputs is crucial. Intensified Design of Experiments (iDoE) is an efficient tool to assess the joint influence of input parameters by including intra-experimental changes. Methods: We applied iDoE to the production phase of a monoclonal antibody in a mammalian bioprocess. The multidimensional design space spanned by temperature, dissolved oxygen (DO), timing of change, and growth category was investigated in 12 cultivations. We built ordinary least squares (OLS) and hybrid models (HM) on the iDoE-data, validated them with classical DoE (cDoE)-derived data, and used the models as in silico representation for process optimization. Results: If the complexity of interactions between changing setpoints of inputs is sufficiently captured during planning and modeling, iDoE proved to be valid for characterizing the mammalian biopharmaceutical production phase. For local behavior and flexible composition of optimization goals, OLS regressions can easily be implemented. To predict global and interconnected dynamics while incorporating mass balances, HM holds potential. Discussion: iDoE will boost protocols that optimize inputs for different bioprocess phases. The described key aspects of OLS- and HM-based analyses of iDoE-data shall guide future applications during manufacturing.
{"title":"Boost dynamic protocols for producing mammalian biopharmaceuticals with intensified DoE—a practical guide to analyses with OLS and hybrid modeling","authors":"V. Nold, L. Junghans, B. Bayer, L. Bisgen, M. Duerkop, R. Drerup, B. Presser, T. Schwab, E. Bluhmki, S. Wieschalka, B. Knapp","doi":"10.3389/fceng.2022.1044245","DOIUrl":"https://doi.org/10.3389/fceng.2022.1044245","url":null,"abstract":"Introduction: For the implementation of robust bioprocesses, understanding of temporal cell behavior with respect to relevant inputs is crucial. Intensified Design of Experiments (iDoE) is an efficient tool to assess the joint influence of input parameters by including intra-experimental changes. Methods: We applied iDoE to the production phase of a monoclonal antibody in a mammalian bioprocess. The multidimensional design space spanned by temperature, dissolved oxygen (DO), timing of change, and growth category was investigated in 12 cultivations. We built ordinary least squares (OLS) and hybrid models (HM) on the iDoE-data, validated them with classical DoE (cDoE)-derived data, and used the models as in silico representation for process optimization. Results: If the complexity of interactions between changing setpoints of inputs is sufficiently captured during planning and modeling, iDoE proved to be valid for characterizing the mammalian biopharmaceutical production phase. For local behavior and flexible composition of optimization goals, OLS regressions can easily be implemented. To predict global and interconnected dynamics while incorporating mass balances, HM holds potential. Discussion: iDoE will boost protocols that optimize inputs for different bioprocess phases. The described key aspects of OLS- and HM-based analyses of iDoE-data shall guide future applications during manufacturing.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91380624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-04DOI: 10.3389/fceng.2022.1072761
Lynette Alvarado-Ramírez, Berenice Santiesteban-Romero, Guillaume Poss, J. E. Sosa-Hernández, Hafiz M. N. Iqbal, R. Parra-Saldívar, A. D. Bonaccorso, Elda M. Melchor-Martínez
The annual global fish production reached a record 178 million tonnes in 2020, which continues to increase. Today, 49% of the total fish is harvested from aquaculture, which is forecasted to reach 60% of the total fish produced by 2030. Considering that the wastes of fishing industries represent up to 75% of the whole organisms, the fish industry is generating a large amount of waste which is being neglected in most parts of the world. This negligence can be traced to the ridicule of the value of this resource as well as the many difficulties related to its valorisation. In addition, the massive expansion of the aquaculture industry is generating significant environmental consequences, including chemical and biological pollution, disease outbreaks that increase the fish mortality rate, unsustainable feeds, competition for coastal space, and an increase in the macroalgal blooms due to anthropogenic stressors, leading to a negative socio-economic and environmental impact. The establishment of integrated multi-trophic aquaculture (IMTA) has received increasing attention due to the environmental benefits of using waste products and transforming them into valuable products. There is a need to integrate and implement new technologies able to valorise the waste generated from the fish and aquaculture industry making the aquaculture sector and the fish industry more sustainable through the development of a circular economy scheme. This review wants to provide an overview of several approaches to valorise marine waste (e.g., dead fish, algae waste from marine and aquaculture, fish waste), by their transformation into biofuels (biomethane, biohydrogen, biodiesel, green diesel, bioethanol, or biomethanol) and recovering biomolecules such as proteins (collagen, fish hydrolysate protein), polysaccharides (chitosan, chitin, carrageenan, ulvan, alginate, fucoidan, and laminarin) and biosurfactants. Graphical Abstract
{"title":"Sustainable production of biofuels and bioderivatives from aquaculture and marine waste","authors":"Lynette Alvarado-Ramírez, Berenice Santiesteban-Romero, Guillaume Poss, J. E. Sosa-Hernández, Hafiz M. N. Iqbal, R. Parra-Saldívar, A. D. Bonaccorso, Elda M. Melchor-Martínez","doi":"10.3389/fceng.2022.1072761","DOIUrl":"https://doi.org/10.3389/fceng.2022.1072761","url":null,"abstract":"The annual global fish production reached a record 178 million tonnes in 2020, which continues to increase. Today, 49% of the total fish is harvested from aquaculture, which is forecasted to reach 60% of the total fish produced by 2030. Considering that the wastes of fishing industries represent up to 75% of the whole organisms, the fish industry is generating a large amount of waste which is being neglected in most parts of the world. This negligence can be traced to the ridicule of the value of this resource as well as the many difficulties related to its valorisation. In addition, the massive expansion of the aquaculture industry is generating significant environmental consequences, including chemical and biological pollution, disease outbreaks that increase the fish mortality rate, unsustainable feeds, competition for coastal space, and an increase in the macroalgal blooms due to anthropogenic stressors, leading to a negative socio-economic and environmental impact. The establishment of integrated multi-trophic aquaculture (IMTA) has received increasing attention due to the environmental benefits of using waste products and transforming them into valuable products. There is a need to integrate and implement new technologies able to valorise the waste generated from the fish and aquaculture industry making the aquaculture sector and the fish industry more sustainable through the development of a circular economy scheme. This review wants to provide an overview of several approaches to valorise marine waste (e.g., dead fish, algae waste from marine and aquaculture, fish waste), by their transformation into biofuels (biomethane, biohydrogen, biodiesel, green diesel, bioethanol, or biomethanol) and recovering biomolecules such as proteins (collagen, fish hydrolysate protein), polysaccharides (chitosan, chitin, carrageenan, ulvan, alginate, fucoidan, and laminarin) and biosurfactants. Graphical Abstract","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47882772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-20DOI: 10.3389/fceng.2022.1084035
J. Oliveras, L. Marcon, N. Bastús, V. Puntes
Emerging pollutants such as pharmaceuticals are of special concern because despite their low environmental concentration, their biological activity can be intense, and they should be prevented to reach uncontrolledly to the environment. A graphene-based hybrid material decorated with Fe3O4 and TiO2 nanoparticles (NPs) has been prepared to effectively remove emerging pollutants as nonsteroidal anti-inflammatory drugs (NSAIDs) Ibuprofen and Diclofenac present in water at low environmental concentrations by a one-step functionalization process following a novel gentle and scalable surfactant depletion approach. Following this methodology, nanoparticles are progressively deprived of their original surfactant in the presence of graphene, leading to the formation of hybrid nanostructures composed of two different types of nanoparticles well dispersed over the graphene nanosheets. Ibuprofen and Diclofenac adsorption kinetics on the composites was investigated via UV-Vis spectroscopy. The as prepared hybrid material possesses high adsorption capacity, superparamagnetic properties, photocatalytic behavior, and good water dispersibility. Thanks to incorporating TiO2 nanoparticles as in situ catalysts, the adsorption performance of composites is restored after use, which could be a promising recycling pathway for the adsorbents in wastewater treatments.
{"title":"Functionalization of graphene nanostructures with inorganic nanoparticles and their use for the removal of pharmaceutical pollutants in water","authors":"J. Oliveras, L. Marcon, N. Bastús, V. Puntes","doi":"10.3389/fceng.2022.1084035","DOIUrl":"https://doi.org/10.3389/fceng.2022.1084035","url":null,"abstract":"Emerging pollutants such as pharmaceuticals are of special concern because despite their low environmental concentration, their biological activity can be intense, and they should be prevented to reach uncontrolledly to the environment. A graphene-based hybrid material decorated with Fe3O4 and TiO2 nanoparticles (NPs) has been prepared to effectively remove emerging pollutants as nonsteroidal anti-inflammatory drugs (NSAIDs) Ibuprofen and Diclofenac present in water at low environmental concentrations by a one-step functionalization process following a novel gentle and scalable surfactant depletion approach. Following this methodology, nanoparticles are progressively deprived of their original surfactant in the presence of graphene, leading to the formation of hybrid nanostructures composed of two different types of nanoparticles well dispersed over the graphene nanosheets. Ibuprofen and Diclofenac adsorption kinetics on the composites was investigated via UV-Vis spectroscopy. The as prepared hybrid material possesses high adsorption capacity, superparamagnetic properties, photocatalytic behavior, and good water dispersibility. Thanks to incorporating TiO2 nanoparticles as in situ catalysts, the adsorption performance of composites is restored after use, which could be a promising recycling pathway for the adsorbents in wastewater treatments.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41589526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-15DOI: 10.3389/fceng.2022.1024259
Shiqi Xu, C. Fan, Peijian Song, Chuanyou Liu
In this paper, the GM(1,1) model with function arccos x transformation and GM(1,1) model with function transformation are established by using arccosine function transformation method and a arccos x function transformation method, and the GM(1,1) model with function cos x 2 transformation is established by using function transformation theory, and GM(1,1) model with function cos x 2 + c transformation is established by using translational transformation theory on the basis of this model. The prediction accuracy of GM(1,1) model, GM(1,1) model with function arccos x transformation, GM(1,1) model with function a arccos x transformation, GM(1,1) model with function cos x 2 transformation, and GM(1,1) model with function cos x 2 + c transformation are compared by modeling with the field pipeline data and the indoor loop data. The influence of a value in GM(1,1) model with function a arccos x transformation on prediction accuracy is discussed, and the influence of c value in GM(1,1) model with function cos x 2 + c transformation on prediction accuracy is discussed. With the increase of a and c values, the average relative error show a trend of decreasing and then increasing, by comparing the average relative errors under different a and c values, the optimal a value and c value and the optimal prediction accuracy are obtained. The results show that the GM(1,1) model with function cos x 2 + c transformation in the indoor loop has an average relative error of 0.6490% when c = 0.114 , which is the minimum average relative error compared to other models and achieves the highest prediction accuracy. The GM(1,1) model with function cos x 2 + c transformation in the field pipeline has an average relative error of 1.94156% when c = − 0.555 , which is the minimum average relative error compared to other models and achieves the highest prediction accuracy. Among the five models, only the GM(1,1) model with function cos x 2 + c transformation has fitted and predicted values that are closer to the actual thickness values in the indoor loop experimental data and the field pipeline data, and the predicted values are more consistent with the actual conditions in the field pipeline. This paper verifies the feasibility of using the GM(1,1) model with function cos x 2 + c transformation to predict the wax deposition thickness of the pipe wall, and provides a reference for subsequent research on accurate prediction of wax deposition thickness.
摘要GM(1, 1)模型和x arccos函数转换和GM(1, 1)模型和函数变换建立了利用余弦函数变换方法和arccos x函数转换方法,和GM(1, 1)模型函数cos x 2转换使用函数转换理论,建立了GM(1, 1)模型和函数因为x 2 + c变换建立了利用平移变换理论的基础上,这个模型。通过与现场管道数据和室内回路数据建模，比较了GM(1,1)模型、GM(1,1)函数arccos x变换模型、GM(1,1)函数arccos x变换模型、GM(1,1)函数cos x2变换模型、GM(1,1)函数cos x2 + c变换模型的预测精度。讨论了函数为arccosx变换的GM(1,1)模型中一个值对预测精度的影响，以及函数为cosx2 + c变换的GM(1,1)模型中c值对预测精度的影响。随着a、c值的增大，平均相对误差呈现先减小后增大的趋势，通过比较不同a、c值下的平均相对误差，得到最优a、c值和最优预测精度。结果表明，当c = 0.114时，室内回路中cos x 2 + c变换的GM(1,1)模型的平均相对误差为0.6490%，是其他模型中平均相对误差最小的模型，预测精度最高。在现场管道中进行cos x 2 + c变换的GM(1,1)模型在c =−0.555时的平均相对误差为1.94156%，与其他模型相比，平均相对误差最小，预测精度最高。在这5个模型中，只有函数cos x 2 + c变换的GM(1,1)模型的拟合预测值更接近室内环实验数据和现场管道数据的实际厚度值，预测值更符合现场管道的实际情况。验证了用函数cos x 2 + c变换的GM(1,1)模型预测管壁蜡沉积厚度的可行性，为后续准确预测蜡沉积厚度的研究提供参考。
{"title":"Prediction of wax deposit thickness in waxy crude oil pipelines using improved GM(1,1) model","authors":"Shiqi Xu, C. Fan, Peijian Song, Chuanyou Liu","doi":"10.3389/fceng.2022.1024259","DOIUrl":"https://doi.org/10.3389/fceng.2022.1024259","url":null,"abstract":"In this paper, the GM(1,1) model with function arccos x transformation and GM(1,1) model with function transformation are established by using arccosine function transformation method and a arccos x function transformation method, and the GM(1,1) model with function cos x 2 transformation is established by using function transformation theory, and GM(1,1) model with function cos x 2 + c transformation is established by using translational transformation theory on the basis of this model. The prediction accuracy of GM(1,1) model, GM(1,1) model with function arccos x transformation, GM(1,1) model with function a arccos x transformation, GM(1,1) model with function cos x 2 transformation, and GM(1,1) model with function cos x 2 + c transformation are compared by modeling with the field pipeline data and the indoor loop data. The influence of a value in GM(1,1) model with function a arccos x transformation on prediction accuracy is discussed, and the influence of c value in GM(1,1) model with function cos x 2 + c transformation on prediction accuracy is discussed. With the increase of a and c values, the average relative error show a trend of decreasing and then increasing, by comparing the average relative errors under different a and c values, the optimal a value and c value and the optimal prediction accuracy are obtained. The results show that the GM(1,1) model with function cos x 2 + c transformation in the indoor loop has an average relative error of 0.6490% when c = 0.114 , which is the minimum average relative error compared to other models and achieves the highest prediction accuracy. The GM(1,1) model with function cos x 2 + c transformation in the field pipeline has an average relative error of 1.94156% when c = − 0.555 , which is the minimum average relative error compared to other models and achieves the highest prediction accuracy. Among the five models, only the GM(1,1) model with function cos x 2 + c transformation has fitted and predicted values that are closer to the actual thickness values in the indoor loop experimental data and the field pipeline data, and the predicted values are more consistent with the actual conditions in the field pipeline. This paper verifies the feasibility of using the GM(1,1) model with function cos x 2 + c transformation to predict the wax deposition thickness of the pipe wall, and provides a reference for subsequent research on accurate prediction of wax deposition thickness.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47117528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-14DOI: 10.3389/fceng.2022.1036867
Simoneta Caño de las Heras, Carina L. Gargalo, Fiammetta Caccavale, K. Gernaey, U. Krühne
Strategies to exploit and enable the digitalization of industrial processes are on course to become game-changers in optimizing (bio)chemical facilities. To achieve this, these industries face an increasing need for process models and, as importantly, an efficient way to store the models and data/information. Therefore, this work proposes developing an online information storage system that can facilitate the reuse and expansion of process models and make them available to the digitalization cycle. This system is named NyctiDB, and it is a novel non-relational database coupled with a bioprocess ontology. The ontology supports the selection and classification of bioprocess models focused information, while the database is in charge of the online storage of said information. Through a series of online collections, NyctiDB contains essential knowledge for the design, monitoring, control, and optimization of a bioprocess based on its mathematical model. Once NyctiDB has been implemented, its applicability and usefulness are demonstrated through two applications. Application A shows how NyctiDB is integrated inside the software architecture of an online educational bioprocess simulator. This implies that NyctiDB provides the information for the visualization of different bioprocess behaviours and the modifications of the models in the software. Moreover, the information related to the parameters and conditions of each model is used to support the users’ understanding of the process. Additionally, application B illustrates that NyctiDB can be used as AI enabler to further the research in this field through open-source and reliable data. This can, in fact, be used as the information source for the AI frameworks when developing, for example, hybrid models or smart expert systems for bioprocesses. Henceforth, this work aims to provide a blueprint on how to collect bioprocess modeling information and connect it to facilitate and empower the Internet-of-Things paradigm and the digitalization of the biomanufacturing industries. Graphical Abstract
{"title":"NyctiDB: A non-relational bioprocesses modeling database supported by an ontology","authors":"Simoneta Caño de las Heras, Carina L. Gargalo, Fiammetta Caccavale, K. Gernaey, U. Krühne","doi":"10.3389/fceng.2022.1036867","DOIUrl":"https://doi.org/10.3389/fceng.2022.1036867","url":null,"abstract":"Strategies to exploit and enable the digitalization of industrial processes are on course to become game-changers in optimizing (bio)chemical facilities. To achieve this, these industries face an increasing need for process models and, as importantly, an efficient way to store the models and data/information. Therefore, this work proposes developing an online information storage system that can facilitate the reuse and expansion of process models and make them available to the digitalization cycle. This system is named NyctiDB, and it is a novel non-relational database coupled with a bioprocess ontology. The ontology supports the selection and classification of bioprocess models focused information, while the database is in charge of the online storage of said information. Through a series of online collections, NyctiDB contains essential knowledge for the design, monitoring, control, and optimization of a bioprocess based on its mathematical model. Once NyctiDB has been implemented, its applicability and usefulness are demonstrated through two applications. Application A shows how NyctiDB is integrated inside the software architecture of an online educational bioprocess simulator. This implies that NyctiDB provides the information for the visualization of different bioprocess behaviours and the modifications of the models in the software. Moreover, the information related to the parameters and conditions of each model is used to support the users’ understanding of the process. Additionally, application B illustrates that NyctiDB can be used as AI enabler to further the research in this field through open-source and reliable data. This can, in fact, be used as the information source for the AI frameworks when developing, for example, hybrid models or smart expert systems for bioprocesses. Henceforth, this work aims to provide a blueprint on how to collect bioprocess modeling information and connect it to facilitate and empower the Internet-of-Things paradigm and the digitalization of the biomanufacturing industries. Graphical Abstract","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44039019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-13DOI: 10.3389/fceng.2022.1048744
Lucía Aristizábal-Lanza, Smita V. Mankar, Cecilia Tullberg, Baozhong Zhang, Javier A. Linares-Pastén
The enzymatic depolymerization of synthetic polyesters has become of great interest in recycling plastics. Most of the research in this area focuses on the depolymerization of polyethylene terephthalate (PET) due to its widespread use in various applications. However, the enzymatic activity on other commercial polyesters is less frequently investigated. Therefore, AkestraTM attracted our attention, which is a copolymer derived from PET with a partially biobased spirocyclic acetal structure. In this study, the activity of Humicola insolens cutinase (HiCut) on PET and AkestraTM films and powder was investigated. HiCut showed higher depolymerization activity on amorphous PET films than on Akestra™ films. However, an outstanding performance was achieved on AkestraTM powder, reaching 38% depolymerization in 235h, while only 12% for PET powder. These results are consistent with the dependence of the enzymes on the crystallinity of the polymer since Akestra™ is amorphous while the PET powder has 14% crystallinity. On the other hand, HiCut docking studies and molecular dynamic simulations (MD) suggested that the PET-derived mono (hydroxyethyl)terephthalate dimer (MHET)2 is a hydrolyzable ligand, producing terephthalic acid (TPA), while the Akestra™-derived TPA-spiroglycol ester is not, which is consistent with the depolymerization products determined experimentally. MD studies also suggest ligand-induced local conformational changes in the active site.
{"title":"Comparison of the enzymatic depolymerization of polyethylene terephthalate and AkestraTM using Humicola insolens cutinase","authors":"Lucía Aristizábal-Lanza, Smita V. Mankar, Cecilia Tullberg, Baozhong Zhang, Javier A. Linares-Pastén","doi":"10.3389/fceng.2022.1048744","DOIUrl":"https://doi.org/10.3389/fceng.2022.1048744","url":null,"abstract":"The enzymatic depolymerization of synthetic polyesters has become of great interest in recycling plastics. Most of the research in this area focuses on the depolymerization of polyethylene terephthalate (PET) due to its widespread use in various applications. However, the enzymatic activity on other commercial polyesters is less frequently investigated. Therefore, AkestraTM attracted our attention, which is a copolymer derived from PET with a partially biobased spirocyclic acetal structure. In this study, the activity of Humicola insolens cutinase (HiCut) on PET and AkestraTM films and powder was investigated. HiCut showed higher depolymerization activity on amorphous PET films than on Akestra™ films. However, an outstanding performance was achieved on AkestraTM powder, reaching 38% depolymerization in 235h, while only 12% for PET powder. These results are consistent with the dependence of the enzymes on the crystallinity of the polymer since Akestra™ is amorphous while the PET powder has 14% crystallinity. On the other hand, HiCut docking studies and molecular dynamic simulations (MD) suggested that the PET-derived mono (hydroxyethyl)terephthalate dimer (MHET)2 is a hydrolyzable ligand, producing terephthalic acid (TPA), while the Akestra™-derived TPA-spiroglycol ester is not, which is consistent with the depolymerization products determined experimentally. MD studies also suggest ligand-induced local conformational changes in the active site.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44974616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.3389/fceng.2022.1066184
S. Pandit, P. Chauhan, A. Sinhamahapatra, Y. Parekh, M. Ghalib Enayathullah, K. Bokara, Aditya Kumar
In this research work, for the first time, we have developed and demonstrated a COVID-19 repellent coating on cotton cloth that not only repels the virus but also most of the human body fluids (superhemophobic). The coating was tested in the BSL3 lab. The controlled experiments revealed no significant increase in the log viral particles on coated fabric compared to the uncoated surface, evidence that the coated fabric resisted the SARS-CoV-2 inoculum. Further, the coated cloth exhibited excellent dust-free nature and stain resistance against body fluids (blood, urine, bovine serum, water, and saliva aerosol). It also shows sufficient robustness for repetitive usage. The fabrication process for the developed COVID-19 repellent cloth is simple and affordable and can be easily scaled up for mass production. Such coating could be applied on various surfaces, including daily clothes, masks, medical clothes, curtains, etc. The present finding could be a mammoth step towards controlling infection spread, including COVID-19. Graphical Abstract
{"title":"COVID-19 repellent cloth","authors":"S. Pandit, P. Chauhan, A. Sinhamahapatra, Y. Parekh, M. Ghalib Enayathullah, K. Bokara, Aditya Kumar","doi":"10.3389/fceng.2022.1066184","DOIUrl":"https://doi.org/10.3389/fceng.2022.1066184","url":null,"abstract":"In this research work, for the first time, we have developed and demonstrated a COVID-19 repellent coating on cotton cloth that not only repels the virus but also most of the human body fluids (superhemophobic). The coating was tested in the BSL3 lab. The controlled experiments revealed no significant increase in the log viral particles on coated fabric compared to the uncoated surface, evidence that the coated fabric resisted the SARS-CoV-2 inoculum. Further, the coated cloth exhibited excellent dust-free nature and stain resistance against body fluids (blood, urine, bovine serum, water, and saliva aerosol). It also shows sufficient robustness for repetitive usage. The fabrication process for the developed COVID-19 repellent cloth is simple and affordable and can be easily scaled up for mass production. Such coating could be applied on various surfaces, including daily clothes, masks, medical clothes, curtains, etc. The present finding could be a mammoth step towards controlling infection spread, including COVID-19. Graphical Abstract","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46420789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-02DOI: 10.3389/fceng.2022.1039675
W. Crawford, D. Tan, F. V. van Ogtrop
There are hundreds of species of Agaves found globally in natural and anthropogenic systems. Agaves are used to produce fibres, alcoholic beverages like tequila, and in biofuel production. The objectives of this study were to assess the research available into Agave planting density and to use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to suggest an optimum planting density for the highest dry aboveground productivity. Background research into Agave planting densities found little data on the effect of planting density on biomass production, with most studies focusing on other independent variables affecting productivity. There were 13 data points included in the analysis. The meta-analysis suggested that the optimal planting density of Agave is approximately 2,600 plants ha−1, which provides optimal dry aboveground biomass of 28.8 Mg ha−1 yr−1. These findings provide a framework for further experimentation in Australian conditions using a Nelder design density experiment to ground-truth the meta-analysis.
{"title":"Optimal planting density of Agave for maximising aboveground biomass: A systematic literature review","authors":"W. Crawford, D. Tan, F. V. van Ogtrop","doi":"10.3389/fceng.2022.1039675","DOIUrl":"https://doi.org/10.3389/fceng.2022.1039675","url":null,"abstract":"There are hundreds of species of Agaves found globally in natural and anthropogenic systems. Agaves are used to produce fibres, alcoholic beverages like tequila, and in biofuel production. The objectives of this study were to assess the research available into Agave planting density and to use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) to suggest an optimum planting density for the highest dry aboveground productivity. Background research into Agave planting densities found little data on the effect of planting density on biomass production, with most studies focusing on other independent variables affecting productivity. There were 13 data points included in the analysis. The meta-analysis suggested that the optimal planting density of Agave is approximately 2,600 plants ha−1, which provides optimal dry aboveground biomass of 28.8 Mg ha−1 yr−1. These findings provide a framework for further experimentation in Australian conditions using a Nelder design density experiment to ground-truth the meta-analysis.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46202490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-30DOI: 10.3389/fceng.2022.1008680
Olivia Murphy, M. Haji
Under the Paris Agreement, established by the United Nations Framework Convention on Climate Change, many countries have agreed to transition their energy sources and technologies to reduce greenhouse gas emissions to levels concordant with the 1.5°C warming goal. Lithium (Li) is critical to this transition due to its use in nuclear fusion as well as in rechargeable lithium-ion batteries used for energy storage for electric vehicles and renewable energy harvesting systems. As a result, the global demand for Li is expected to reach 5.11 Mt by 2050. At this consumption rate, the Li reserves on land are expected to be depleted by 2080. In addition to spodumene and lepidolite ores, Li is present in seawater, and salt-lake brines as dissolved Li+ ions. Li recovery from aqueous solutions such as these are a potential solution to limited terrestrial reserves. The present work reviews the advantages and challenges of a variety of technologies for Li recovery from aqueous solutions, including precipitants, solvent extractants, Li-ion sieves, Li-ion-imprinted membranes, battery-based electrochemical systems, and electro-membrane-based electrochemical systems. The techno-economic feasibility and key performance parameters of each technology, such as the Li+ capacity, selectivity, separation efficiency, recovery, regeneration, cyclical stability, thermal stability, environmental durability, product quality, extraction time, and energy consumption are highlighted when available. Excluding precipitation and solvent extraction, these technologies demonstrate a high potential for sustainable Li+ extraction from low Li+ concentration aqueous solutions or seawater. However, further research and development will be required to scale these technologies from benchtop experiments to industrial applications. The development of optimized materials and synthesis methods that improve the Li+ selectivity, separation efficiency, chemical stability, lifetime, and Li+ recovery should be prioritized. Additionally, techno-economic and life cycle analyses are needed for a more critical evaluation of these extraction technologies for large-scale Li production. Such assessments will further elucidate the climate impact, energy demand, capital costs, operational costs, productivity, potential return on investment, and other key feasibility factors. It is anticipated that this review will provide a solid foundation for future research commercialization efforts to sustainably meet the growing demand for Li as the world transitions to clean energy.
{"title":"A review of technologies for direct lithium extraction from low Li+ concentration aqueous solutions","authors":"Olivia Murphy, M. Haji","doi":"10.3389/fceng.2022.1008680","DOIUrl":"https://doi.org/10.3389/fceng.2022.1008680","url":null,"abstract":"Under the Paris Agreement, established by the United Nations Framework Convention on Climate Change, many countries have agreed to transition their energy sources and technologies to reduce greenhouse gas emissions to levels concordant with the 1.5°C warming goal. Lithium (Li) is critical to this transition due to its use in nuclear fusion as well as in rechargeable lithium-ion batteries used for energy storage for electric vehicles and renewable energy harvesting systems. As a result, the global demand for Li is expected to reach 5.11 Mt by 2050. At this consumption rate, the Li reserves on land are expected to be depleted by 2080. In addition to spodumene and lepidolite ores, Li is present in seawater, and salt-lake brines as dissolved Li+ ions. Li recovery from aqueous solutions such as these are a potential solution to limited terrestrial reserves. The present work reviews the advantages and challenges of a variety of technologies for Li recovery from aqueous solutions, including precipitants, solvent extractants, Li-ion sieves, Li-ion-imprinted membranes, battery-based electrochemical systems, and electro-membrane-based electrochemical systems. The techno-economic feasibility and key performance parameters of each technology, such as the Li+ capacity, selectivity, separation efficiency, recovery, regeneration, cyclical stability, thermal stability, environmental durability, product quality, extraction time, and energy consumption are highlighted when available. Excluding precipitation and solvent extraction, these technologies demonstrate a high potential for sustainable Li+ extraction from low Li+ concentration aqueous solutions or seawater. However, further research and development will be required to scale these technologies from benchtop experiments to industrial applications. The development of optimized materials and synthesis methods that improve the Li+ selectivity, separation efficiency, chemical stability, lifetime, and Li+ recovery should be prioritized. Additionally, techno-economic and life cycle analyses are needed for a more critical evaluation of these extraction technologies for large-scale Li production. Such assessments will further elucidate the climate impact, energy demand, capital costs, operational costs, productivity, potential return on investment, and other key feasibility factors. It is anticipated that this review will provide a solid foundation for future research commercialization efforts to sustainably meet the growing demand for Li as the world transitions to clean energy.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48314686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-29DOI: 10.3389/fceng.2022.1059305
Vidhisha Jassal, Chang Dou, Ning Sun, Seema Singh, B. Simmons, Hemant Choudhary
This article outlines the technical and economic potentials of lignin in unlocking sustainable biorefineries. The benefits of using this highly functionalized biopolymer for the growth of sustainable economy have been highlighted. But practically, the possibility of commercially substituting petroleum oil with lignin is still not very high as the estimated biofuel production cost is 2–3 times higher than the former one. However, with the advancement in technology and more efficient measures by biorefineries such as storing and processing the biomass near the field so as to reduce the transportation cost, it is possible to gain higher profits. Companies like Domtar, Stora Enso, Borregaard’s LignoTech, VITO, and Chemelot InSciTe have been promoting commercial value of lignin. The growth of lignin market after the start-up production at various sites has been discussed in this review. Combining the complete “start-to-finish” analysis with economic evaluation gives a pragmatic overview of the possibilities whether lignin will join petroleum oil as an efficient and cost-effective renewable source.
{"title":"Finding values in lignin: A promising yet under-utilized component of the lignocellulosic biomass","authors":"Vidhisha Jassal, Chang Dou, Ning Sun, Seema Singh, B. Simmons, Hemant Choudhary","doi":"10.3389/fceng.2022.1059305","DOIUrl":"https://doi.org/10.3389/fceng.2022.1059305","url":null,"abstract":"This article outlines the technical and economic potentials of lignin in unlocking sustainable biorefineries. The benefits of using this highly functionalized biopolymer for the growth of sustainable economy have been highlighted. But practically, the possibility of commercially substituting petroleum oil with lignin is still not very high as the estimated biofuel production cost is 2–3 times higher than the former one. However, with the advancement in technology and more efficient measures by biorefineries such as storing and processing the biomass near the field so as to reduce the transportation cost, it is possible to gain higher profits. Companies like Domtar, Stora Enso, Borregaard’s LignoTech, VITO, and Chemelot InSciTe have been promoting commercial value of lignin. The growth of lignin market after the start-up production at various sites has been discussed in this review. Combining the complete “start-to-finish” analysis with economic evaluation gives a pragmatic overview of the possibilities whether lignin will join petroleum oil as an efficient and cost-effective renewable source.","PeriodicalId":73073,"journal":{"name":"Frontiers in chemical engineering","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41371038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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