Effect of Nanocarbon on the Structural and Mechanical Properties of 6061 Aluminum Composites by Powder Metallurgy.

6061 aluminum composites with 0.5 and 1 vol. % graphene nanoplatelets as well as 1 and 2 vol. % activated nanocarbon were manufactured by a powder metallurgy method. Scanning electron microscopy and Raman spectroscopy were used to study the morphology, structure, and distribution of nanocarbon reinforcements in the composite samples. Density Functional Theory (DFT) calculations were performed to understand the aluminum-carbon bonding and the effects of hybridized networks of carbon atoms on nanocarbon aluminum matrix composites. Scanning electron microscopy showed the good distribution and low agglomeration tendencies of nanoparticles in the composites. The formation of secondary phases at the materials interface was not detected in the hot-pressed composites. Raman spectroscopy showed structural changes in the reinforced composites after the manufacturing process. The results from Density Functional Theory calculations suggest that it is thermodynamically possible to form carbon rings in the aluminum matrix, which may be responsible for the improved mechanical strength. Our results also suggest that these carbon networks are graphene-like, which also agrees with the Raman spectroscopy data. Micro-Vickers hardness and compressive tests were used to determine the mechanical properties of the samples. Composites presented enhanced hardness, yield and ultimate strength compared to the 6061 aluminum alloy with no nanocarbon reinforcement. Ductility was also affected, as shown by the reduction in elongation and by the number of dimples in the fractured surfaces of the materials.

Trends in in-silico guided engineering of efficient polyethylene terephthalate (PET) hydrolyzing enzymes to enable bio-recycling and upcycling of PET.

Polyethylene terephthalate (PET) is the largest produced polyester globally, and less than 30% of all the PET produced globally (∼6 billion pounds annually) is currently recycled into lower-quality products. The major drawbacks in current recycling methods (mechanical and chemical), have inspired the exploration of potentially efficient and sustainable PET depolymerization using biological approaches. Researchers have discovered efficient PET hydrolyzing enzymes in the plastisphere and have demonstrated the selective degradation of PET to original monomers thus enabling biological recycling or upcycling. However, several significant hurdles such as the less efficiency of the hydrolytic reaction, low thermostability of the enzymes, and the inability of the enzyme to depolymerize crystalline PET must be addressed in order to establish techno-economically feasible commercial-scale biological PET recycling or upcycling processes. Researchers leverage a synthetic biology-based design; build, test, and learn (DBTL) methodology to develop commercially applicable efficient PET hydrolyzing enzymes through 1) high-throughput metagenomic and proteomic approaches to discover new PET hydrolyzing enzymes with superior properties: and, 2) enzyme engineering approaches to modify and optimize PET hydrolyzing properties. Recently, in-silico platforms including molecular mechanics and machine learning concepts are emerging as innovative tools for the development of more efficient and effective PET recycling through the exploration of novel mutations in PET hydrolyzing enzymes. In-silico-guided PET hydrolyzing enzyme engineering with DBTL cycles enables the rapid development of efficient variants of enzymes over tedious conventional enzyme engineering methods such as random or directed evolution. This review highlights the potential of in-silico-guided PET degrading enzyme engineering to create more efficient variants, including Ideonella sakaiensis PETase (IsPETase) and leaf-branch compost cutinases (LCC). Furthermore, future research prospects are discussed to enable a sustainable circular economy through the bioconversion of PET to original or high-value platform chemicals.

Sensitive Detection of the Human Epididymis Protein-4 (HE4) Ovarian Cancer Biomarker through a Sandwich-Type Immunoassay Method with Laser-Induced Breakdown Spectroscopy.

Detection of cancer before the appearance of any symptoms is crucial for successful treatment. Early detection is, however, very challenging, particularly for the types of cancer with few or no symptoms at early stages, such as epithelial ovarian cancer (EOC). Developing a user-friendly method that can detect biomarkers with sufficient selectivity, sensitivity, and reproducibility is a promising approach for overcoming the challenges of early detection of EOC. In this study, we report a sandwich-type microparticle immunoassay for sensitive detection of the HE4 biomarker with laser-induced breakdown spectroscopy. Here, we cross-linked elemental particles to a specific functional group of the targeted biomolecules based on a covalent and non-covalent linking chemistry to improve the sensitivity and selectivity of biomarker detection, in which Fe3O4 and SiO2 microparticles were used to conjugate and purify the antibody-antigen in complex media. Simultaneous detection of Fe and Si from a magnetically purified assay significantly improves the HE4 biomarker's detectability, in which HE4 was detected with a limit of detection of 0.0022 pM. We also determined the coupling ratio between HE4 and silica particles using a silicon calibration curve.

Carbon Nanotube Based Robust and Flexible Solid-State Supercapacitor.

All solid-state flexible electrochemical double-layer capacitors (EDLCs) are crucial for providing energy options in a variety of applications, ranging from wearable electronics to bendable micro/nanotechnology. Here, we report on the development of robust EDLCs using aligned multiwalled carbon nanotubes (MWCNTs) grown directly on thin metal foils embedded in a poly(vinyl alcohol)/phosphoric acid (PVA/H3PO4) polymer gel. The thin metal substrate holding the aligned MWCNT assembly provides mechanical robustness and the PVA/H3PO4 polymer gel, functioning both as the electrolyte as well as the separator, provides sufficient structural flexibility, without any loss of charge storage capacity under flexed conditions. The performance stability of these devices was verified by testing them under straight and bent formations. A high value of the areal specific capacitance (CSP) of ∼14.5 mF cm-2 with an energy density of ∼1 µW h cm-2 can be obtained in these devices. These values are significantly higher (in some cases, orders of magnitude) than several graphene as well as single-walled nanotube-based EDLC's utilizing similar electrolytes. We further show that these devices can withstand multiple (∼2500) mechanical bending cycles, without losing their energy storage capacities and are functional within the temperature range of 20 to 70 °C. Several strategies for enhancing the capacitive charge storage, such as physically stacking (in parallel) individual devices, or postproduction thermal annealing of electrodes, are also demonstrated. These findings demonstrated in this article provide tremendous impetus toward the realization of robust, stackable, and flexible all solid-state supercapacitors.

Sequence-Independent DNA Adsorption on Few-Layered Oxygen-Functionalized Graphene Electrodes: An Electrochemical Study for Biosensing Application.

DNA is strongly adsorbed on oxidized graphene surfaces in the presence of divalent cations. Here, we studied the effect of DNA adsorption on electrochemical charge transfer at few-layered, oxygen-functionalized graphene (GOx) electrodes. DNA adsorption on the inkjet-printed GOx electrodes caused amplified current response from ferro/ferricyanide redox probe at concentration range 1 aM-10 nM in differential pulse voltammetry. We studied a number of variables that may affect the current response of the interface: sequence type, conformation, concentration, length, and ionic strength. Later, we showed a proof-of-concept DNA biosensing application, which is free from chemical immobilization of the probe and sensitive at attomolar concentration regime. We propose that GOx electrodes promise a low-cost solution to fabricate a highly sensitive platform for label-free and chemisorption-free DNA biosensing.

Influence of Glutaraldehyde's Molecular Transformations on Spectroscopic Investigations of Its Conjugation with Amine-Modified Fe3O4 Microparticles in the Reaction Medium.

Glutaraldehyde (GA) is a widely used cross-linking agent in biological research due to its superior characteristics, such as high reactivity toward proteins, high stability, and cost-effectiveness. In this regard, analyzing spectral changes initiated by various molecular forms and transformations of GA in a reaction medium and its reaction with surface functional-modified solid spheres is vital for a successful bioconjugation process targeting the biomolecules of interest. In this work, we present Fourier transform-infrared (FT-IR), Raman, and UV-visible spectroscopic analyses of glutaraldehyde-modified Fe3O4 microparticles (magnetic beads) to confirm the conjugation between GA and magnetic beads. We also studied the molecular transformations of glutaraldehyde during the reaction with amine-modified magnetic beads via investigating the reaction medium of the glutaraldehyde solution. Our FT-IR and Raman studies confirmed that glutaraldehyde was successfully coupled on the magnetic beads. Furthermore, FT-IR and UV-vis studies on the glutaraldehyde solution revealed the multiple molecular forms of GA in an aqueous medium, and they also confirmed that glutaraldehyde transforms into other molecular forms while the reaction occurs with the magnetic beads.

Machine Learning Approach to Raman Spectrum Analysis of MIA PaCa-2 Pancreatic Cancer Tumor Repopulating Cells for Classification and Feature Analysis.

A machine learning approach is applied to Raman spectra of cells from the MIA PaCa-2 human pancreatic cancer cell line to distinguish between tumor repopulating cells (TRCs) and parental control cells, and to aid in the identification of molecular signatures. Fifty-one Raman spectra from the two types of cells are analyzed to determine the best combination of data type, dimension size, and classification technique to differentiate the cell types. An accuracy of 0.98 is obtained from support vector machine (SVM) and k-nearest neighbor (kNN) classifiers with various dimension reduction and feature selection tools. We also identify some possible biomolecules that cause the spectral peaks that led to the best results.

High Adsorption of Benzoic Acid on Single Walled Carbon Nanotube Bundles.

Removal of harmful chemicals from water is paramount to environmental cleanliness and safety. As such, need for materials that will serve this purpose is in the forefront of environmental research that pertains to water purification. Here we show that bundles of single walled carbon nanotubes (SWNTs), synthesized by direct thermal decomposition of ferrocene (Fe(C5H5)2), can remove emerging contaminants like benzoic acid from water with high efficiencies. Experimental adsorption isotherm studies indicate that the sorption capacity of benzoic acid on these carbon nanotubes (CNTs) can be as high as 375 mg/g, which is significantly higher (in some cases an order of magnitude) than those reported previously for other adsorbents of benzoic acid such as activated carbon cloth, modified bentonite and commercially available graphitized multiwall carbon nanotubes (MWNTs). Our Molecular Dynamics (MD) simulation studies of experimental scenarios provided major insights related to this process of adsorption. The MD simulations indicate that, high binding energy sites present in SWNT bundles are majorly responsible for their enhanced adsorptive behavior compared to isolated MWNTs. These findings indicate that SWNT materials can be developed as scalable materials for efficient removal of environmental contaminants as well as for other sorption-based applications.

Modeling of laser-induced breakdown spectroscopic data analysis by an automatic classifier.

Laser-induced breakdown spectroscopy (LIBS) is a multi-elemental and real-time analytical technique with simultaneous detection of all the elements in any type of sample matrix including solid, liquid, gas, and aerosol. LIBS produces vast amount of data which contains information on elemental composition of the material among others. Classification and discrimination of spectra produced during the LIBS process are crucial to analyze the elements for both qualitative and quantitative analysis. This work reports the design and modeling of optimal classifier for LIBS data classification and discrimination using the apparatus of statistical theory of detection. We analyzed the noise sources associated during the LIBS process and created a linear model of an echelle spectrograph system. We validated our model based on assumptions through statistical analysis of "dark signal" and laser-induced breakdown spectra from the database of National Institute of Science and Technology. The results obtained from our model suggested that the quadratic classifier provides optimal performance if the spectroscopy signal and noise can be considered Gaussian.

Femtosecond Laser Ablation Synthesis of Aryl Functional Group Substituted Gold Nanoparticles.

Femtosecond laser ablation synthesis of gold-aryl nanoparticles in solution was explored. Laser irradiation of the yellow solution of diazonium tetrachloroaurate(III) salt [C8F17-4-C6H4N≡N]AuCl4 in acetonitrile formed ruby red color of gold-aryl nanoparticles without the need for external chemical reducing agent. X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), X-ray powder diffraction (XRD), and nanodrop UV-Vis spectroscopy were used in the nanoparticles characterization. XPS showed the presence of the core­shell and the formation of gold(0) oxidation state only. The nanoparticles size distribution estimated by TEM is dependent on the duration of laser irradiation. Longer irradiation time resulted in decreasing the nanoparticles size. UV-Vis studies in acetonitrile showed that the absorption of gold(III) at 310 nm vanished with a concomitant formation of a plasmon absorbance at 532 nm due to the formation of "embryonic" gold-aryl nanoparticles. The novelty of this work is the in situ conjugation of core­shell structure without the need for adjusting the conjugate/gold ratio, chemicals-free synthesis from reducing agents and surfactants, synthesis of nanoparticles using gold salts unlike the common ablation of flat metal surfaces, and the use of reactive [AuCl4]− counter-ion that permits the co-deposition of gold and conjugates. Released solvated electrons and hydrogen radicals are believed to induce the reduction reaction of the gold salts. Isolation of pure nanoparticles is important for further biomedical applications including cellular uptake.

Tag-femtosecond laser-induced breakdown spectroscopy for the sensitive detection of cancer antigen 125 in blood plasma.

Successful treatment of cancers requires detecting early signs of the disease. One promising way to approach this is to develop minimally invasive tests for the sensitive and specific detection of biomarkers in blood. Irrespective of the detection approach one uses, this remains a challenging task because biomarkers are typically present in low concentrations and there are signals that interfere strongly with prevailing compounds of human fluids. In this paper, we show that elemental encoded particle assay coupled with femtosecond laser-induced breakdown spectroscopy for simultaneous multi-elemental analysis can significantly improve biomarker detectability. An estimated near single molecule per particle efficiency of this method leads to sensitive detection of ovarian cancer biomarker CA125 in human blood plasma. This work opens new ways for earlier detection of cancers and for multiplex assay developments in various analytical applications from proteomics, genomics, and neurology fields.