Effect of Post-Washing on Textural Characteristics of Carbon Materials Derived from Pineapple Peel Biomass.

Porous carbon materials have been widely used to remove pollutants from the liquid-phase streams. However, their limited pore properties could be a major problem. In this work, the effects of post-washing methods (i.e., water washing and acid washing) on the textural characteristics of the resulting biochar and activated carbon products from pineapple peel biomass were investigated in the carbonization and CO2 activation processes. The experiments were set at an elevated temperature (i.e., 800 °C) holding for 30 min. It was found that the enhancement in pore property reached about a 50% increase rate, increasing from 569.56 m2/g for the crude activated carbon to the maximal BET surface area of 843.09 m2/g for the resulting activated carbon by water washing. The resulting activated carbon materials featured the microporous structures but also were characteristic of the mesoporous solids. By contrast, the enhancement in the increase rate by about 150% was found in the resulting biochar products. However, there seemed to be no significant variations in pore property with post-washing methods. Using the energy dispersive X-ray spectroscopy (EDS) and the Fourier Transform infrared spectroscopy (FTIR) analyses, it showed some oxygen-containing functional groups or complexes, potentially posing the hydrophilic characters on the surface of the resulting carbon materials.

Production of Porous Biochar from Cow Dung Using Microwave Process.

To valorize livestock manure, the present study investigated the production of biochar from cow dung (CD) by microwave pyrolysis. The pore properties and chemical characteristics of CD and CD-based biochar products were found to correlate with the process parameters like microwave power (300-1000 W) and residence time (5-20 min). The findings indicated that CD is an excellent biomass based on the richness of lignocellulosic constituents from the results of proximate analysis and thermogravimetric analysis (TGA). Higher calorific values were obtained at mild microwave conditions, giving the maximal enhancement factor 139% in comparison with the calorific value of CD (18.97 MJ/kg). Also, it can be concluded that the biochar product obtained at 800 W for a holding time of 5 min had the maximal BET surface area of 127 m2/g and total pore volume of 0.104 cm3/g, which were microporous and mesoporous in the nitrogen adsorption-desorption adsorption analysis. On the other hand, the CD-based biochar contained oxygen-containing functional groups and inorganic minerals based on the spectroscopic analyses by Fourier-transform infrared spectroscopy (FTIR) and energy-dispersive X-ray spectroscopy (EDS), thus featuring to be prone to hydrophilicity in aqueous solutions.

Analysis of changes in greenhouse gas emissions and technological approaches for achieving carbon neutrality by 2050 in Taiwan.

Over the past two decades, the Taiwan government promulgated some regulatory measures and promotional actions on energy efficiency promotion and renewable energy development. In March 2022, the "Taiwan's Pathway to Net-Zero Emissions in 2050" was announced to respond to the Paris Agreement. In order to achieve the goal, the Climate Change Response Act (CCRA) was passed on February 15, 2023, requiring the de-carbonization measures and adaptation strategies. The main aim of this paper was to analyze the changes in GHG emissions and renewable energy supply by using the updated data from the official statistics in connection with the trends of environmental and energy sustainability since 2000. The findings showed that total installed capacity of renewable power (especially in solar power and wind power) showed an amazing increase over the past decade, leading to the inclined GHG emissions and thus supporting the environmental and energy sustainability toward a low-carbon society. Furthermore, this paper summarized the development history and main differences concerning the carbon neutrality policy and legislation in Japan and South Korea. For achieving the staged targets of GHG emissions by 2030 and 2050, this paper finally addressed the technological approaches for achieving carbon neutrality by 2050 in Taiwan, focusing on the transformation of energy and industry, and the policy implications by all levels of government.

Optimization of Physical Activation Process by CO2 for Activated Carbon Preparation from Honduras Mahogany Pod Husk.

In this work, the Honduras Mahogany (Swietenia macropnylla King, SMK) seed husk was used as a novel biomass resource for producing activated carbon by physical activation. The texture characteristics and chemical characterization of resulting products were investigated in correlation with the process parameters. Based on the thermochemical properties of the SMK biomass, the process conditions were set to a rate of about 10 °C/min under nitrogen (N2) flow of 500 cm3/min heated to 500 °C, then switched to carbon dioxide (CO2) flow of 100 cm3/min in the specified activation conditions (i.e., temperature of 700-850 °C for holding times of 0-60 min). Our findings showed that the texture characteristics (i.e., surface area and pore volume) increased with an activation temperature increase from 700 to 800 °C for a holding time of 30 min but gradually decreased as the temperature increased thereafter. Similarly, the texture characteristics also indicated an increasing trend with the residence time extending from 0 min to 30 min but slightly decreased as the time was extended to 60 min. Therefore, the optimal activation conditions for producing SMK-based activated carbon should be set at 800 °C for a holding time of 30 min to obtain the maximal texture characteristics (i.e., BET surface area of 966 m2/g and total pore volume of 0.43 cm3/g). On the other hand, the chemical characteristics were analyzed by energy dispersive X-ray spectroscopy (EDS) and Fourier Transform infrared spectroscopy (FTIR), showing oxygen complexes contained on the hydrophilic surface of the resulting activated carbon.

Production of Mesoporous Magnetic Carbon Materials from Oily Sludge by Combining Thermal Activation and Post-Washing.

In this work, the oily sludge (OS) from a local waste oil recycling plant was reused as a precursor for producing porous magnetic carbon composites (CC) by pyrolysis, followed by carbon dioxide activation. Based on the thermogravimetric analysis (TGA) of the OS feedstock, the preparation experiments were performed at 800−900 °C. From the pore analysis of the CC products, it indicated an increasing trend, as the BET surface area greatly increased from about 1.0 to 44.30 m2/g. In addition, the enhancement effect on the pore properties can be consistently obtained from the acid-washed CC products because the existing and new pores were reformed due to the leaching-out of inorganic minerals. It showed an increase from 32.27 to 94.45 m2/g and 44.30 to 94.52 m2/g at 850 and 900 °C, respectively, showing their mesoporous features. These porous and iron-containing features were also observed by the scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS). In addition, the adsorption removal of total organic carbon (TOC) in the raw wastewater, by the CC product, showed its high performance (>80%).

Preparation and Characterization of Porous Materials from Pineapple Peel at Elevated Pyrolysis Temperatures.

In this work, pineapple peel (PP) was reused as a precursor in biochar (BC) production at elevated temperatures (i.e., 500−900 °C) for residence times of 0−60 min. The findings showed that pyrolysis temperature and residence time played a vital role in pore development. As pyrolysis temperature increased from 800 to 900 °C for residence times of 20 and 60 min, the data on the Brunauer−Emmett−Teller (BET) surface area of the resulting biochar products significantly jumped from 11.98−32.34 to 119.43−133.40 m2/g. In addition, there was a significant increase in the BET surface area from 1.02 to 133.40 m2/g with the residence time of 0 to 20 min at 900 °C. From the data of the nitrogen adsorption−desorption isotherms and the pore size distribution, both micropores (pore diameters of <2.0 nm) and mesopores (pore diameters of 2.0−50.0 nm) are present in the PP-based biochar products. Due to its good fittings in the pseudo-second-order model and its hydrophilic nature, as seen in the Fourier transform infrared spectroscopy (FTIR), the resulting biochar could be a porous material to be used for the effective removal of cationic compounds (i.e., methylene blue (MB)) from liquid phases.

Reuse of the Materials Recycled from Renewable Resources in the Civil Engineering: Status, Achievements and Government's Initiatives in Taiwan.

Growing concerns about the circular economy and sustainable waste management for civil applications of non-hazardous mineral industrial waste have increased in recent years. Therefore, this study presents a trend analysis of industrial waste generation and treatment during the years of 2010-2020, and focused on promotion policies and regulatory measures for mandatory renewable resources from industrial sources in Taiwan, including reclaimed asphalt pavement (RAP) material, water-quenched blast furnace slag, and ilmenite chlorination furnace slag. According to the official database of the online reported statistics during the period of 2010-2020, approximately three million metric tons per year of renewable resources were totally reused in civil engineering or related cement products, reflecting a balanced supply chain in the domestic market. Among these, water-quenched blast furnace slag accounted for about 90% (about 2.7 million metric tons) in Taiwan. Currently, the legislative framework of sustainable waste management in Taiwan is based on the Waste Management Act and the Resource Recycling Act, but there are some problems with them. In order to effectively reduce environmental loadings and conserve natural resources to mitigate climate change, some recommendations are addressed from different points of view.

POINeT: protein interactome with sub-network analysis and hub prioritization.

BACKGROUND: Protein-protein interactions (PPIs) are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. RESULTS: We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3) to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. CONCLUSION: The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to selected tissues can be revealed. The straightforward interface of POINeT makes PPI search and analysis just a few clicks away. The modular design permits further functional enhancement without hampering the simplicity. POINeT is available at (http://poinet.bioinformatics.tw/).

Ortholog-based protein-protein interaction prediction and its application to inter-species interactions.

BACKGROUND: The rapid growth of protein-protein interaction (PPI) data has led to the emergence of PPI network analysis. Despite advances in high-throughput techniques, the interactomes of several model organisms are still far from complete. Therefore, it is desirable to expand these interactomes with ortholog-based and other methods. RESULTS: Orthologous pairs of 18 eukaryotic species were expanded and merged with experimental PPI datasets. The contributions of interologs from each species were evaluated. The expanded orthologous pairs enable the inference of interologs for various species. For example, more than 32,000 human interactions can be predicted. The same dataset has also been applied to the prediction of host-pathogen interactions. PPIs between P. falciparum calmodulin and several H. sapiens proteins are predicted, and these interactions may contribute to the maintenance of host cell Ca2+ concentration. Using comparisons with Bayesian and structure-based approaches, interactions between putative HSP40 homologs of P. falciparum and the H. sapiens TNF receptor associated factor family are revealed, suggesting a role for these interactions in the interference of the human immune response to P. falciparum. CONCLUSION: The PPI datasets are available from POINT http://point.bioinformatics.tw/ and POINeT http://poinet.bioinformatics.tw/. Further development of methods to predict host-pathogen interactions should incorporate multiple approaches in order to improve sensitivity, and should facilitate the identification of targets for drug discovery and design.

Bioinformatics approaches for disulfide connectivity prediction.

Protein structure prediction with computational methods has gained much attention in the research fields of protein engineering and protein folding studies. Due to the vastness of conformational space, one of the major tasks is to restrain the flexibility of protein structure and reduce the search space. Many studies have revealed that, with the information of disulfide connectivity available, the search in conformational space can be dramatically reduced and lead to significant improvements in the prediction accuracy. As a result, predicting disulfide connectivity using bioinformatics approaches is of great interest nowadays. In this mini-review, the prediction of disulfide connectivity in proteins will be discussed in four aspects: (1) how the problem formulated and the computational techniques used in the literatures; (2) the effects of the features adopted to encode the information and the biological meanings implied; (3) the problems encountered and limitations of disulfide connectivity prediction; and (4) the practical usages of predicted disulfide bond information in molecular simulation and the prospects in the future.

Disulfide connectivity prediction with 70% accuracy using two-level models.

Disulfide bridges stabilize protein structures covalently and play an important role in protein folding. Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. Previous methods for disulfide connectivity prediction either infer the bonding potential of cysteine pairs or rank alternative disulfide bonding patterns. As a result, these methods encode data according to cysteine pairs (pair-wise) or disulfide bonding patterns (pattern-wise). However, using either encoding scheme alone cannot fully utilize the local and global information of proteins, so the accuracies of previous methods are limited. In this work, we propose a novel two-level framework to predict disulfide connectivity. With this framework, both the pair-wise and pattern-wise encoding schemes are considered. Our models were validated on the datasets derived from SWISS-PROT 39 and 43, and the results demonstrate that our models can combine both local and global information. Compared to previous methods, significant improvements were obtained by our models. Our work may also provide insights to further improvements of disulfide connectivity prediction and increase its applicability in protein structure analysis and prediction.

Improving disulfide connectivity prediction with sequential distance between oxidized cysteines.

SUMMARY: Predicting disulfide connectivity precisely helps towards the solution of protein structure prediction. In this study, a descriptor derived from the sequential distance between oxidized cysteines (denoted as DOC) is proposed. An approach using support vector machine (SVM) method based on weighted graph matching was further developed to predict the disulfide connectivity pattern in proteins. When DOC was applied, prediction accuracy of 63% for our SVM models could be achieved, which is significantly higher than those obtained from previous approaches. The results show that using the non-local descriptor DOC coupled with local sequence profiles significantly improves the prediction accuracy. These improvements demonstrate that DOC, with a proper scaling scheme, is an effective feature for the prediction of disulfide connectivity. The method developed in this work is available at the web server PreCys (prediction of cys-cys linkages of proteins).

Cysteine separations profiles on protein sequences infer disulfide connectivity.

MOTIVATION: Disulfide bonds play an important role in protein folding. A precise prediction of disulfide connectivity can strongly reduce the conformational search space and increase the accuracy in protein structure prediction. Conventional disulfide connectivity predictions use sequence information, and prediction accuracy is limited. Here, by using an alternative scheme with global information for disulfide connectivity prediction, higher performance is obtained with respect to other approaches. RESULT: Cysteine separation profiles have been used to predict the disulfide connectivity of proteins. The separations among oxidized cysteine residues on a protein sequence have been encoded into vectors named cysteine separation profiles (CSPs). Through comparisons of their CSPs, the disulfide connectivity of a test protein is inferred from a non-redundant template set. For non-redundant proteins in SwissProt 39 (SP39) sharing less than 30% sequence identity, the prediction accuracy of a fourfold cross-validation is 49%. The prediction accuracy of disulfide connectivity for proteins in SwissProt 43 (SP43) is even higher (53%). The relationship between the similarity of CSPs and the prediction accuracy is also discussed. The method proposed in this work is relatively simple and can generate higher accuracies compared to conventional methods. It may be also combined with other algorithms for further improvements in protein structure prediction. AVAILABILITY: The program and datasets are available from the authors upon request. CONTACT: cykao@csie.ntu.edu.tw.

GEM: a Gaussian Evolutionary Method for predicting protein side-chain conformations.

We have developed an evolutionary approach to predicting protein side-chain conformations. This approach, referred to as the Gaussian Evolutionary Method (GEM), combines both discrete and continuous global search mechanisms. The former helps speed up convergence by reducing the size of rotamer space, whereas the latter, integrating decreasing-based Gaussian mutations and self-adaptive Gaussian mutations, continuously adapts dihedrals to optimal conformations. We tested our approach on 38 proteins ranging in size from 46 to 325 residues and showed that the results were comparable to those using other methods. The average accuracies of our predictions were 80% for chi(1), 66% for chi(1 + 2), and 1.36 A for the root mean square deviation of side-chain positions. We found that if our scoring function was perfect, the prediction accuracy was also essentially perfect. However, perfect prediction could not be achieved if only a discrete search mechanism was applied. These results suggest that GEM is robust and can be used to examine the factors limiting the accuracy of protein side-chain prediction methods. Furthermore, it can be used to systematically evaluate and thus improve scoring functions.