Comparison of the effect of ultrasounic-harmonic scalpel and electrocautery in the treatment of axillary lymph nodes during radical surgery for breast cancer.

OBJECTIVE: To compare the efficacy of ultrasounic-harmonic scalpel and electrocautery in the treatment of axillary lymph nodes during radical surgery for breast cancer. METHODS: A prospective study was conducted in the Department of Breast Surgery, Zhongda Hospital Affiliated to Southeast University. A total of 128 patients with pathologically confirmed breast cancer who were treated by the same surgeon from July 2023 to November 2023 were included in the analysis. All breast operations were performed using electrocautery, and surgical instruments for axillary lymph nodes were divided into ultrasounic-harmonic scalpel group and electrocautery group using a random number table. According to the extent of lymph node surgery, it was divided into four groups: sentinel lymph node biopsy, lymph node at station I, lymph node at station I and II, and lymph node dissection at station I, II and III. Under the premise of controlling variables such as BMI, age and neoadjuvant chemotherapy, the effects of ultrasounic-harmonic scalpel and electrocautery in axillary surgery were compared. RESULTS: Compared with the electrosurgical group, there were no significant differences in lymph node operation time, intraoperative blood loss, postoperative axillary drainage volume, axillary drainage tube indwelling time, postoperative pain score on the day after surgery, and the incidence of postoperative complications (p>0.05). CONCLUSION: There is no significant difference between ultrasounic-harmonic scalpel and electrocautery in axillary lymph node treatment for breast cancer patients, which can provide a basis for the selection of surgical energy instruments.

Recent Advances in 4D Printing of Advanced Materials and Structures for Functional Applications.

4D printing has attracted tremendous worldwide attention during the past decade. This technology enables the shape, property, or functionality of printed structures to change with time in response to diverse external stimuli, making the original static structures alive. The revolutionary 4D-printing technology offers remarkable benefits in controlling geometric and functional reconfiguration, thereby showcasing immense potential across diverse fields, including biomedical engineering, electronics, robotics, and photonics. Here, a comprehensive review of the latest achievements in 4D printing using various types of materials and different additive manufacturing techniques is presented. The state-of-the-art strategies implemented in harnessing various 4D-printed structures are highlighted, which involve materials design, stimuli, functionalities, and applications. The machine learning approach explored for 4D printing is also discussed. Finally, the perspectives on the current challenges and future trends toward further development in 4D printing are summarized.

Differences of serum glucose and lipid metabolism and immune parameters and blood metabolomics regarding the transition cows in the antepartum and postpartum period.

This study aims to investigate differences in metabolism regarding the transition cows. Eight cows were selected for the test. Serum was collected on antepartum days 14th (ap14) and 7th (ap7) and postpartum days 1st (pp1), 7th (pp7), and 14th (pp14) to detect biochemical parameters. The experiment screened out differential metabolites in the antepartum (ap) and postpartum (pp) periods and combined with metabolic pathway analysis to study the relationship and role between metabolites and metabolic abnormalities. Results: (1) The glucose (Glu) levels in ap7 were significantly higher than the other groups (p < 0.01). The insulin (Ins) levels of ap7 were significantly higher than pp7 (p = 0.028) and pp14 (p < 0.01), and pp1 was also significantly higher than pp14 (p = 0.016). The insulin resistance (HOMA-IR) levels of ap7 were significantly higher than ap14, pp7, and pp14 (p < 0.01). The cholestenone (CHO) levels of ap14 and pp14 were significantly higher than pp1 (p < 0.01). The CHO levels of pp14 were significantly higher than pp7 (p < 0.01). The high density lipoprotein cholesterol (DHDL) levels of pp1 were significantly lower than ap14 (p = 0.04), pp7 (p < 0.01), and pp14 (p < 0.01), and pp14 was also significantly higher than ap14 and ap7 (p < 0.01). (2) The interferon-gamma (IFN-γ) and tumor necrosis factor α (TNF-α) levels of ap7 were significantly higher than pp1 and pp7 (p < 0.01); the immunoglobulin A (IgA) levels of pp1 were significantly higher than ap7 and pp7 (p < 0.01); the interleukin-4 (IL-4) levels of pp7 were significantly higher than ap7 and pp1 (p < 0.01), the interleukin-6 (IL-6) levels of ap7 and pp1 were significantly higher than pp7 (p < 0.01). (3) Metabolomics identified differential metabolites mainly involved in metabolic pathways, such as tryptophan metabolism, alpha-linolenic acid metabolism, tyrosine metabolism, and lysine degradation. The main relevant metabolism was concentrated in lipid and lipid-like molecules, organic heterocyclic compounds, organic acids, and their derivatives. The results displayed the metabolic changes in the transition period, which laid a foundation for further exploring the mechanism of metabolic abnormalities in dairy cows in the transition period.

Additively Manufactured Flexible Liquid Metal-Coated Self-Powered Magnetoelectric Sensors with High Design Freedom.

Although additive manufacturing enables controllable structural design and customized performance for magnetoelectric sensors, their design and fabrication still require careful matching of the size and modulus between the magnetic and conductive components. Achieving magnetoelectric integration remains challenging, and the rigid coils limit the flexibility of the sensors. To overcome these obstacles, this study proposes a composite process combining selective laser sintering (SLS) and 3D transfer printing for fabricating flexible liquid metal-coated magnetoelectric sensors. The liquid metal forms a conformal conductive network on the SLS-printed magnetic lattice structure. Deformation of the structure alters the magnetic flux passing through it, thereby generating voltage. A reverse model segmentation and summation method is established to calculate the theoretical magnetic flux. The impact of the volume fraction, unit size, and height of the sensors on the voltage is studied, and optimization of these factors yields a maximum voltage of 45.6 µV. The sensor has excellent sensing performance with a sensitivity of 10.9 kPa-1 and a minimum detection pressure of 0.1 kPa. The voltage can be generated through various external forces. This work presents a significant advancement in fabricating liquid metal-based magnetoelectric sensors by improving their structural flexibility, magnetoelectric integration, and design freedom.

Discovering the Active Ingredients of Medicine and Food Homologous Substances for Inhibiting the Cyclooxygenase-2 Metabolic Pathway by Machine Learning Algorithms.

Cyclooxygenase-2 (COX-2) and microsomal prostaglandin E2 synthase (mPGES-1) are two key targets in anti-inflammatory therapy. Medicine and food homology (MFH) substances have both edible and medicinal properties, providing a valuable resource for the development of novel, safe, and efficient COX-2 and mPGES-1 inhibitors. In this study, we collected active ingredients from 503 MFH substances and constructed the first comprehensive MFH database containing 27,319 molecules. Subsequently, we performed Murcko scaffold analysis and K-means clustering to deeply analyze the composition of the constructed database and evaluate its structural diversity. Furthermore, we employed four supervised machine learning algorithms, including support vector machine (SVM), random forest (RF), deep neural networks (DNNs), and eXtreme Gradient Boosting (XGBoost), as well as ensemble learning, to establish 640 classification models and 160 regression models for COX-2 and mPGES-1 inhibitors. Among them, ModelA_ensemble_RF_1 emerged as the optimal classification model for COX-2 inhibitors, achieving predicted Matthews correlation coefficient (MCC) values of 0.802 and 0.603 on the test set and external validation set, respectively. ModelC_RDKIT_SVM_2 was identified as the best regression model based on COX-2 inhibitors, with root mean squared error (RMSE) values of 0.419 and 0.513 on the test set and external validation set, respectively. ModelD_ECFP_SVM_4 stood out as the top classification model for mPGES-1 inhibitors, attaining MCC values of 0.832 and 0.584 on the test set and external validation set, respectively. The optimal regression model for mPGES-1 inhibitors, ModelF_3D_SVM_1, exhibited predictive RMSE values of 0.253 and 0.35 on the test set and external validation set, respectively. Finally, we proposed a ligand-based cascade virtual screening strategy, which integrated the well-performing supervised machine learning models with unsupervised learning: the self-organized map (SOM) and molecular scaffold analysis. Using this virtual screening workflow, we discovered 10 potential COX-2 inhibitors and 15 potential mPGES-1 inhibitors from the MFH database. We further verified candidates by molecular docking, investigated the interaction of the candidate molecules upon binding to COX-2 or mPGES-1. The constructed comprehensive MFH database has laid a solid foundation for the further research and utilization of the MFH substances. The series of well-performing machine learning models can be employed to predict the COX-2 and mPGES-1 inhibitory capabilities of unknown compounds, thereby aiding in the discovery of anti-inflammatory medications. The COX-2 and mPGES-1 potential inhibitor molecules identified through the cascade virtual screening approach provide insights and references for the design of highly effective and safe novel anti-inflammatory drugs.

Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability.

In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.

Classification of FLT3 inhibitors and SAR analysis by machine learning methods.

FMS-like tyrosine kinase 3 (FLT3) is a type III receptor tyrosine kinase, which is an important target for anti-cancer therapy. In this work, we conducted a structure-activity relationship (SAR) study on 3867 FLT3 inhibitors we collected. MACCS fingerprints, ECFP4 fingerprints, and TT fingerprints were used to represent the inhibitors in the dataset. A total of 36 classification models were built based on support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and deep neural networks (DNN) algorithms. Model 3D_3 built by deep neural networks (DNN) and TT fingerprints performed best on the test set with the highest prediction accuracy of 85.83% and Matthews correlation coefficient (MCC) of 0.72 and also performed well on the external test set. In addition, we clustered 3867 inhibitors into 11 subsets by the K-Means algorithm to figure out the structural characteristics of the reported FLT3 inhibitors. Finally, we analyzed the SAR of FLT3 inhibitors by RF algorithm based on ECFP4 fingerprints. The results showed that 2-aminopyrimidine, 1-ethylpiperidine,2,4-bis(methylamino)pyrimidine, amino-aromatic heterocycle, [(2E)-but-2-enyl]dimethylamine, but-2-enyl, and alkynyl were typical fragments among highly active inhibitors. Besides, three scaffolds in Subset_A (Subset 4), Subset_B, and Subset_C showed a significant relationship to inhibition activity targeting FLT3.

Prediction and Structure-Activity Relationship Analysis on Ready Biodegradability of Chemical Using Machine Learning Method.

Persistent contaminants from different industries have already caused significant risks to the environment and public health. In this study, a data set containing 1306 not readily biodegradable (NRB) and 622 readily biodegradable (RB) chemicals was collected and characterized by CORINA descriptors, MACCS fingerprints, and ECFP_4 fingerprints. We utilized decision tree (DT), support vector machine (SVM), random forest (RF), and deep neural network (DNN) to construct 34 classification models that could predict the biodegradability of compounds. The best model (model 5F) built using a Transformer-CNN algorithm had a balanced accuracy of 86.29% and a Matthews correlation coefficient of 0.71 on the test set. By analyzing the top 10 CORINA descriptors used for modeling, the properties containing solubility, π/σ atom charges, rotatable bonds number, lone pair/π/σ atom electronegativities, molecular weight, and number of nitrogen atom based hydrogen bonding acceptors were determined to be critical for biodegradability. The substructure investigations confirmed earlier studies that the presence of aromatic rings and nitrogen or halogen substitutions in a molecule will hinder the biodegradation of the compound, while the ester groups and carboxyl groups promote biodegradability. We also identified the representative fragments affecting biodegradability by analyzing the frequency differences of substructural fragments between the NRB and RB compounds. The results of the study can provide excellent guidance for the discovery and design of compounds with good chemical biodegradability.

Prediction of bioactivities of microsomal prostaglandin E2 synthase-1 inhibitors by machine learning algorithms.

There is a strong interest in the development of microsomal prostaglandin E2 synthase-1 (mPGES-1) inhibitors of their potential to safely and effectively treat inflammation. Herein, 70 QSAR models were built on the dataset (735 mPGES-1 inhibitors) characterized with RDKit descriptors by multiple linear regression (MLR), support vector machine (SVM), random forest (RF), deep neural networks (DNN), and eXtreme Gradient Boosting (XGBoost). The other three regression models on the dataset are represented by SMILES using self-attention recurrent neural networks (RNN) and Graph Convolutional Networks (GCN). For the best model (Model C2), which was developed by SVM with RDKit descriptors, the coefficient of determination (R2 ) of 0.861 and root mean squared error (RMSE) of 0.235 were achieved for the test set. Additionally, R2 of 0.692 and RMSE of 0.383 were obtained on the external test set. We investigated the applicability domain (AD) of Model C2 with the rivality index (RI), the prediction of Model C2 on 78.92% of molecules in the test set, and 78.33% of molecules in the external test set were reliable. After dissecting the RDKit descriptors of Model C2, we found important physicochemical properties of highly active mPGES-1 inhibitors. Besides, by analyzing the attention weight of each atom of each inhibitor from the attention layer, we found that the benzamide group and the trifluoromethyl cyclohexane group are favorable substructures for mPGES-1 inhibitors.

Spontaneous FeIII/FeII redox cycling in single-atom catalysts: Conjugation effect and electron delocalization.

The mechanism of spontaneous FeIII/FeII redox cycling in iron-centered single-atom catalysts (I-SACs) is often overlooked. Consequently, pathways for continuous SO4 ·-/HO⋅ generation during peroxymonosulfate (PMS) activation by I-SACs remain unclear. Herein, the evolution of the iron center and ligand in I-SACs was comprehensively investigated. I-SACs could be considered as a coordination complex created by iron and a heteroatom N-doped carbonaceous ligand. The ligand-field theory could well explain the electronic behavior of the complex, whereby electrons delocalized by the conjugation effect of the ligand were confirmed to be responsible for the FeIII/FeII redox cycle. The possible pyridinic ligand in I-SACs was demonstrably weaker than the pyrrolic ligand in FeIII reduction due to its shielding effect on delocalized π orbitals by local lone-pair electrons. The results of this study significantly advance our understanding of the mechanism of spontaneous FeIII/FeII redox cycling and radical generation pathways in the I-SACs/PMS process.

Classification models and SAR analysis on HDAC1 inhibitors using machine learning methods.

Histone deacetylase (HDAC) 1, a member of the histone deacetylases family, plays a pivotal role in various tumors. In this study, we collected 7313 human HDAC1 inhibitors with bioactivities to form a dataset. Then, the dataset was divided into a training set and a test set using two splitting methods: (1) Kohonen's self-organizing map and (2) random splitting. The molecular structures were represented by MACCS fingerprints, RDKit fingerprints, topological torsions fingerprints and ECFP4 fingerprints. A total of 80 classification models were built by using five machine learning methods, including decision tree (DT), random forest, support vector machine, eXtreme Gradient Boosting and deep neural network. Model 15A_2 built by the XGBoost algorithm based on ECFP4 fingerprints showed the best performance, with an accuracy of 88.08% and an MCC value of 0.76 on the test set. Finally, we clustered the 7313 HDAC1 inhibitors into 31 subsets, and the substructural features in each subset were investigated. Moreover, using DT algorithm we analyzed the structure-activity relationship of HDAC1 inhibitors. It may conclude that some substructures have a significant effect on high activity, such as N-(2-amino-phenyl)-benzamide, benzimidazole, AR-42 analogues, hydroxamic acid with a middle chain alkyl and 4-aryl imidazole with a midchain of alkyl whose α carbon is chiral.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.

BACKGROUND: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis. Accordingly, we initiated the development of the community-based Coronavirus Infectious Disease Ontology (CIDO) in early 2020. RESULTS: As an Open Biomedical Ontology (OBO) library ontology, CIDO is open source and interoperable with other existing OBO ontologies. CIDO is aligned with the Basic Formal Ontology and Viral Infectious Disease Ontology. CIDO has imported terms from over 30 OBO ontologies. For example, CIDO imports all SARS-CoV-2 protein terms from the Protein Ontology, COVID-19-related phenotype terms from the Human Phenotype Ontology, and over 100 COVID-19 terms for vaccines (both authorized and in clinical trial) from the Vaccine Ontology. CIDO systematically represents variants of SARS-CoV-2 viruses and over 300 amino acid substitutions therein, along with over 300 diagnostic kits and methods. CIDO also describes hundreds of host-coronavirus protein-protein interactions (PPIs) and the drugs that target proteins in these PPIs. CIDO has been used to model COVID-19 related phenomena in areas such as epidemiology. The scope of CIDO was evaluated by visual analysis supported by a summarization network method. CIDO has been used in various applications such as term standardization, inference, natural language processing (NLP) and clinical data integration. We have applied the amino acid variant knowledge present in CIDO to analyze differences between SARS-CoV-2 Delta and Omicron variants. CIDO's integrative host-coronavirus PPIs and drug-target knowledge has also been used to support drug repurposing for COVID-19 treatment. CONCLUSION: CIDO represents entities and relations in the domain of coronavirus diseases with a special focus on COVID-19. It supports shared knowledge representation, data and metadata standardization and integration, and has been used in a range of applications.

Profiling COVID-19 Vaccine Adverse Events by Statistical and Ontological Analysis of VAERS Case Reports.

Since the beginning of the COVID-19 pandemic, vaccines have been developed to mitigate the spread of SARS-CoV-2, the virus that causes COVID-19. These vaccines have been effective in reducing the rate and severity of COVID-19 infection but also have been associated with various adverse events (AEs). In this study, data from the Vaccine Adverse Event Reporting System (VAERS) was queried and analyzed via the Cov19VaxKB vaccine safety statistical analysis tool to identify statistically significant (i.e., enriched) AEs for the three currently FDA-authorized or approved COVID-19 vaccines. An ontology-based classification and literature review were conducted for these enriched AEs. Using VAERS data as of 31 December 2021, 96 AEs were found to be statistically significantly associated with the Pfizer-BioNTech, Moderna, and/or Janssen COVID-19 vaccines. The Janssen COVID-19 vaccine had a higher crude reporting rate of AEs compared to the Moderna and Pfizer COVID-19 vaccines. Females appeared to have a higher case report frequency for top adverse events compared to males. Using the Ontology of Adverse Event (OAE), these 96 adverse events were classified to different categories such as behavioral and neurological AEs, cardiovascular AEs, female reproductive system AEs, and immune system AEs. Further statistical comparison between different ages, doses, and sexes was also performed for three notable AEs: myocarditis, GBS, and thrombosis. The Pfizer vaccine was found to have a closer association with myocarditis than the other two COVID-19 vaccines in VAERS, while the Janssen vaccine was more likely to be associated with thrombosis and GBS AEs. To support standard AE representation and study, we have also modeled and classified the newly identified thrombosis with thrombocytopenia syndrome (TTS) AE and its subclasses in the OAE by incorporating the Brighton Collaboration definition. Notably, severe COVID-19 vaccine AEs (including myocarditis, GBS, and TTS) rarely occur in comparison to the large number of COVID-19 vaccinations administered in the United States, affirming the overall safety of these COVID-19 vaccines.

Recent Advances on High-Performance Polyaryletherketone Materials for Additive Manufacturing.

Polyaryletherketone (PAEK) is emerging as an important high-performance polymer material in additive manufacturing (AM) benefiting from its excellent mechanical properties, good biocompatibility, and high-temperature stability. The distinct advantages of AM facilitate the rapid development of PAEK products with complex customized structures and functionalities, thereby enhancing their applications in various fields. Herein, the recent advances on AM of high-performance PAEKs are comprehensively reviewed, concerning the materials properties, AM processes, mechanical properties, and potential applications of additively manufactured PAEKs. To begin, an introduction to fundamentals of AM and PAEKs, as well as the advantages of AM of PAEKs is provided. Discussions are then presented on the material properties, AM processes, processing-matter coupling mechanism, thermal conductivity, crystallization characteristics, and microstructures of AM-processed PAEKs. Thereafter, the mechanical properties and anisotropy of additively manufactured PAEKs are discussed in depth. Their representative applications in biomedical, aerospace, electronics, and other fields are systematically presented. Finally, current challenges and possible solutions are discussed for the future development of high-performance AM polymers.

Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

3D-Printed Anisotropic Polymer Materials for Functional Applications.

Anisotropy is the characteristic of a material to exhibit variations in its mechanical, electrical, thermal, optical properties, etc. along different directions. Anisotropic materials have attracted great research interest because of their wide applications in aerospace, sensing, soft robotics, and tissue engineering. 3D printing provides exceptional advantages in achieving controlled compositions and complex architecture, thereby enabling the manufacture of 3D objects with anisotropic functionalities. Here, a comprehensive review of the recent progress on 3D printing of anisotropic polymer materials based on different techniques including material extrusion, vat photopolymerization, powder bed fusion, and sheet lamination is presented. The state-of-the-art strategies implemented in manipulating anisotropic structures are highlighted with the discussion of material categories, functionalities, and potential applications. This review is concluded with analyzing the current challenges and providing perspectives for further development in this field.

3D Printing and Chemical Dealloying of a Hierarchically Micro- and Nanoporous Catalyst for Wastewater Purification.

Hierarchically porous-structured materials show tremendous potential for catalytic applications. In this work, a facile method through the combination of three-dimensional (3D) printing and chemical dealloying was employed to synthesize a nanoporous-copper-encapsulating microporous-diamond-cellular-structure (NPC@DCS) catalyst. The developed NPC@DCS catalyst was utilized as a heterogeneous photo-Fenton-like catalyst where its catalytic applications in the remediation of organic wastewater were exemplified. The experimental results demonstrated that the NPC@DCS catalyst possessed a remarkable degradation efficiency in the removal of rhodamine B with a reaction rate of 8.24 × 10-2 min-1 and displayed attractive stability, durability, mineralization capability, and versatility. This work not only manifests the applicability of the proposed NPC@DCS catalyst for wastewater purification in practical applications but also is anticipated to inspire the incorporation of the 3D printing technology and chemical synthesis to design high-performance metal catalysts with tunable hierarchical micro- and nanopores for functional applications.

Effects of maize straw treated with various levels of CaO and moisture on composition, structure, and digestion by in vitro gas production.

OBJECTIVE: The objective of this study was to explore the effects of maize straw treated with calcium oxide (CaO) and various moisture, on the composition and molecular structure of the fiber, and gas production by fermentation in an in vitro rumen environment. METHODS: The experiment used 4×3 Factorial treatment. Maize straws were treated with 4 concentrations of CaO (0%, 3%, 5%, and 7% of dry straw weight) and 3 moisture contents (40%, 50%, and 60%). Scanning electron microscopy, Fourier transform infrared spectroscopy and X-ray fluorescence spectroscopy were employed to measure the surface texture, secondary molecular structure of carbohydrate, and calcium (Ca) content of the maize straw, respectively. The correlation of secondary molecular structures and fiber components of maize straw were analyzed by CORR procedure of SAS 9.2. In vitro rumen fermentation was performed for 6, 12, 24, 48, and 72 h to measure gas production. RESULTS: Overall, the moisture factor had no obvious effect on the experimental results. Neutral detergent fiber (NDF), acid detergent fiber, acid detergent lignin, hemicellulose and cellulose contents decreased (p<0.05) with increasing concentrations of CaO treatment. Surface and secondary molecular structure of maize straw were affected by various CaO and moisture treatments. NDF had positive correlation (p<0.01) with Cell-H (H, height), Cell-A (A, area), CHO-2-H. Hemicellulose had positive correlation (p<0.01) with Lignin-H, Lignin-A, Cell-H, Cell-A. Ca content of maize straw increased as the concentration of CaO was increased (p<0.01). Gas production was highest in the group treated with 7% CaO. CONCLUSION: CaO can adhere to the surface of the maize straw, and then improve the digestibility of the maize straw in ruminants by modifying the structure of lignocellulose and facilitating the maize straw for microbial degradation.

Capillary-Force-Driven Self-Assembly of 4D-Printed Microstructures.

Capillary-force-driven self-assembly is emerging as a significant approach for the massive manufacture of advanced materials with novel wetting, adhesion, optical, mechanical, or electrical properties. However, academic value and practical applications of the self-assembly are greatly restricted because traditional micropillar self-assembly is always unidirectional. In this work, two-photon-lithography-based 4D microprinting is introduced to realize the reversible and bidirectional self-assembly of microstructures. With asymmetric crosslinking densities, the printed vertical microstructures can switch to a curved state with controlled thickness, curvature, and smooth morphology that are impossible to replicate by traditional 3D-printing technology. In different evaporating solvents, the 4D-printed microstructures can experience three states: (I) coalesce into clusters from original vertical states via traditional self-assembly, (II) remain curved, or (III) arbitrarily self-assemble (4D self-assembly) toward the curving directions. Compared to conventional approaches, this 4D self-assembly is distance-independent, which can generate varieties of assemblies with a yield as high as 100%. More importantly, the three states can be reversibly switched, allowing the development of many promising applications such as reversible micropatterns, switchable wetting, and dynamic actuation of microrobots, origami, and encapsulation.

Selective Laser Sintering-Based 4D Printing of Magnetism-Responsive Grippers.

Components fabricated by four-dimensional (4D) printing hold the potential for applications in soft robotics because of their characteristics of responding to external stimuli. Grippers, being the common structures used in robotics, were fabricated by the selective laser sintering (SLS)-based 4D printing of magnetism-responsive materials and tested for remote-controllable deformation in an external magnetic field. A composite material consisting of magnetic Nd2Fe14B powder and thermoplastic polyurethane powder was selected as the raw material for the SLS; the magnetic particle acquired permanent magnetism by magnetization after the SLS process. Microscopic characterization showed the homogeneous dispersion of magnetic particles inside the polymer matrix. The magnetic induction intensity distribution was systematically investigated by both experiments and numerical simulations. The reliability of the numerical model proposed was justified by the excellent consistency between them. The deformation of the grippers could be regulated by tuning the magnetic particle content and the distance from the external magnet; the deformation mechanism is investigated numerically. The magnetic driving force and the corresponding horizontal displacement are calculated, thus having high accuracy compared with the existing research that obtained the deformation amount by only visual inspection. Mechanical properties of the SLS-fabricated magnetic polymer composite specimens were also studied because of their close relationship with the deformation behaviors. These findings provide guidance for future research on controllable deformation and driving force calculation for 4D printing.