Nanoparticle Clusters Research Articles

Toroidal-dipole-resonance–induced reversal of the lateral optical force on plasmonic nanoparticle clusters

Toroidal-dipole-resonance–induced reversal of the lateral optical force on plasmonic nanoparticle clusters

Investigation the structural and surface characteristics of irradiated flexible polymeric nanocomposites films

This study is to investigate the surface and structural characteristics of the pure and irradiated novel PEO/NiO composite by subjecting the films to argon ions with different ion beam fluencies. The structural characteristics were studied by the EDX and FTIR techniques, while the surface was investigated by SEM technique. The FTIR showed a notable decrease in the peak intensity for the bombarded composite, due to the functional groups with hydrophilic characteristics and the occurrence of chain scission processes. The PEO/NiO composite demonstrates a consistent structure without any nanoparticle clusters, as depicted in the SEM image of PEO/NiO. Moreover, the electrical conductivity for the pure and the irradiated samples were determined. Exposing the composite PEO/NiO to a fluence of 15×1016 ions.cm-2, increasing the conductivity from 7.5×10–8 S/cm to 8.4×10–7 S/cm. By increasing ion fluence from 5×1016 to 15×1016 ions.cm-2. The contact angle is decreased from 81.15o to 72.22o for water, while is decreased from 74.32o to 62.20o for diiodomethane. Moreover, the surface wettability and the adhesion force were determined from the data of the contact angle. The work of adhesion of water increases from 84.37 to 94.16 mJ/m2 and for dioodomethane from 64.52 to 74.49 mJ/m2 , respectively, by increasing ion fluence from 5×1016 to 15×1016 ions.cm-2. This suggests that, in comparison to a unirradiated surface, the increase in 𝑊𝑊𝑎𝑎 is the result of surface cleanliness following radiationThe results of this study show the opportunities for utilizing these irradiated materials in the fields of coating and printing applications.

Surface-enhanced infrared spectroscopic study of extracellular vesicles using plasmonic gold nanoparticles

Extracellular vesicles (EVs), sub-micrometer lipid-bound particles released by most cells, are considered a novel area in both biology and medicine. Among characterization methods, infrared (IR) spectroscopy, especially attenuated total reflection (ATR), is a rapidly emerging label-free tool for molecular characterization of EVs. The relatively low number of vesicles in biological fluids (~1010 particle/mL), however, and the complex content of the EVs’ milieu (protein aggregates, lipoproteins, buffer molecules) might result in poor signal-to-noise ratio in the IR analysis of EVs. Exploiting the increment of the electromagnetic field at the surface of plasmonic nanomaterials, surface-enhanced infrared spectroscopy (SEIRS) provides an amplification of characteristic IR signals of EV samples. Negatively charged citrate-capped and positively charged cysteamine-capped gold nanoparticles with around 10nm diameter were synthesized and tested with blood-derived EVs. Both types of gold nanoparticles contributed to an enhancement of the EVs’ IR spectroscopic signature. Joint evaluation of UV-Vis and IR spectroscopic results, supported by FF-TEM images, revealed that proper interaction of gold nanoparticles with EVs is crucial, and an aggregation or clustering of gold nanoparticles is necessary to obtain the SEIRS effect. Positively charged gold nanoparticles resulted in higher enhancement, probably due to electrostatic interaction with EVs, commonly negatively charged.

Dependence of Thermal Conductivity on Size and Specific Surface Area for Different Based CoFe2O4 Cluster Nanofluids

Enhancing the thermal conductivity of fluids by using nanoparticles with outstanding thermophysical properties has acquired significant attention for heat-transfer applications. Nanofluids have the potential to optimize energy systems by improving heat-transfer efficiency. In this study, cobalt ferrite nanoparticles clusters with controlled mean sizes ranging from 97 to 192 nm were synthesized using a solvothermal method to develop novel nanofluids with enhanced thermal conductivity. These clusters were comprehensively characterized using transmission electron microscopy, X-ray diffraction, Raman spectroscopy, vibrating-sample magnetometry, and nitrogen physisorption. The CoFe2O4 cluster nanofluids were prepared using the two-step method with various base fluids (water, propylene glycol, and a mixture of both). Dynamic light scattering analyses of the average Z-size of the dispersed nanoadditives over time revealed that the stability of the dispersions is influenced by cluster size and the proportion of glycol in the base fluid. The thermal conductivity of the base fluid and nine different 0.5 wt% CoFe2O4 cluster nanofluids was measured using the transient hot wire method at temperatures of 293.15, 303.15, and 313.15 K, showing different temperature dependencies. The study also explores the relationships between the thermal conductivity, cluster size, and specific surface area of the nanoadditives. A maximum thermal conductivity enhancement of 4.2% was reported for the 0.5 wt% nanofluid based on propylene glycol containing 97 nm CoFe2O4 clusters. The findings suggest that the specific surface area of nanostructures is a more relevant parameter than size for describing improvements in thermal conductivity.

RGO-g-C3N4-Co3O4 Composite Modified Platinum Electrode for Electrochemical Detection of Dopamine

Abstract A novel RGO-g-C3N4-Co3O4 composite modified platinum electrode with significant sensing performance for dopamine was fabricated. Herein, RGO-g-C3N4-Co3O4 hybrid nanostructure could boost the electrocatalytic performance of nanoparticles by avoiding the clustering of nanoparticles. These spinel-based composites are stable and affordable materials. Electrochemical impedance spectroscopy revealed enhanced electron transfer at the modified electrode, as evidenced by the lowest charge transfer resistance (Rct) for the RGO-g-C₃N₄-Co₃O₄/Pt electrode. An increased electroactive surface area compared to bare and other modified Pt electrodes was obtained. Several experimental parameters were optimized to maximize sensitivity, including the choice of supporting electrolyte and pH. Cyclic voltammetry conducted at varying scan rates confirmed that the oxidation of dopamine followed a diffusion-controlled process. The modified electrode exhibits outstanding electrocatalytic activity, with a detection limit as low as 8.10×10−7M, demonstrating a wide linear range between 2.00×10−6 M to 4×10−4 M. Selectivity tests indicated that the sensor could reliably detect dopamine in the presence of common interfering substances such as NaCl, KCl, glucose and urea, ascorbic acid, uric acid and L-dopa. This enhanced sensitivity and selectivity were validated in both synthetic blood and urine samples, providing the electrode’s potential for real-world applications in the diagnosis of neurodegenerative diseases.

Deep learning-assisted morphological segmentation for effective particle area estimation and prediction of interfacial properties in polymer composites.

The link between the macroscopic properties of polymer nanocomposites and the underlying microstructural features necessitates an understanding of nanoparticle dispersion. The dispersion of nanoparticles introduces variability, potentially leading to clustering and localized accumulation of nanoparticles. This non-uniform dispersion impacts the accuracy of predictive models. In response to this challenge, this study developed an automated and precise technique for particle recognition and detailed mapping of particle positions in scanning electron microscopy (SEM) micrographs. This was achieved by integrating deep convolutional neural networks with advanced image processing techniques. Following particle detection, two dispersion factors were introduced, namely size uniformity and supercritical clustering, to quantify the impact of particle dispersion on properties. These factors, estimated using the computer vision technique, were subsequently used to calculate the effective load-bearing area influenced by the particles. An adapted micromechanical model was then employed to quantify the interfacial strength and thickness of the nanocomposites. This approach enabled the establishment of a correlation between dispersion characteristics and interfacial properties by integrating experimental data, relevant micromechanical models, and quantified dispersion factors. The proposed systematic procedure demonstrates considerable promise in utilizing deep learning to capture and quantify particle dispersion characteristics for structure-property analyses in polymer nanocomposites.

Intracellular Redox Environment Determines Cancer-normal Cell Selectivity of Selenium Nanoclusters.

Elucidating the chemical structure and intracellular action mechanisms is still the critical limit for the clinical translation of nanomedicines. Intracellular redox environments originating from cell metabolism are key factors affecting internalized drug efficacy. Herein, we engineer Se-Se/Se-S bond to assemble selenium nanoclusters with intracellular redox environment-driven selective structure. Chemical structure analysis reveals that, the bonding of sulfur atom in intermediates to the two neighboring or interposition selenium atoms in selenium rings is the key internal driving force for nanoparticle cluster formation. This nanocluster can be predominantly transformed to selenocysteine to facilitate selenoproteins synthesis in normal cells, while metabolize to cytotoxic SeO32- based on the oxidative intracellular redox environment of cancer cells. Resultantly, selenium nanoclusters exhibit significant cell proliferation inhibition ability to cancer cells and impressive safety to normal cells. Taken together, this study not only clarifies the chemical nature of the atom engineering of selenium nanocluster, but also elucidates its intracellular redox environment-oriented anticancer mechanism.

Intracellular Redox Environment Determines Cancer‐normal Cell Selectivity of Selenium Nanoclusters

Elucidating the chemical structure and intracellular action mechanisms is still the critical limit for the clinical translation of nanomedicines. Intracellular redox environments originating from cell metabolism are key factors affecting internalized drug efficacy. Herein, we engineer Se‐Se/Se‐S bond to assemble selenium nanoclusters with intracellular redox environment‐driven selective structure. Chemical structure analysis reveals that, the bonding of sulfur atom in intermediates to the two neighboring or interposition selenium atoms in selenium rings is the key internal driving force for nanoparticle cluster formation. This nanocluster can be predominantly transformed to selenocysteine to facilitate selenoproteins synthesis in normal cells, while metabolize to cytotoxic SeO32‐ based on the oxidative intracellular redox environment of cancer cells. Resultantly, selenium nanoclusters exhibit significant cell proliferation inhibition ability to cancer cells and impressive safety to normal cells. Taken together, this study not only clarifies the chemical nature of the atom engineering of selenium nanocluster, but also elucidates its intracellular redox environment‐oriented anticancer mechanism.

Aggregation/agglomeration dependent percolation threshold of spherical nanoparticles in electrically conductive polymer nanocomposites

AbstractThis study evaluated the percolation threshold in polymer nanocomposites and its dependence on the aggregation/agglomeration phenomenon. The main objective of the investigation was to propose a particular method that besides revealing the impact of different involved parameters on the percolation threshold could also provide useful information regarding the system characteristics. In line with that, a specific geometrical structure was employed to represent the clusters and assess the variation of the percolation threshold and characteristics of the polymer/particle interphase region. The findings were applied in an improved form of the percolation theory to perform validation against the related experimental data. Improvement was conducted by involving the impact of the newly estimated percolation threshold, aggregations/agglomerations, interphase region, and so forth. Also, specific theories were employed to define the electrical conductivity of the nanoparticle clusters and interphase region. The results showed that the percolation threshold is directly affected by the content of the nanoparticles and the characteristics of the aggregates/agglomerates. Besides, it was revealed that parameters, such as the size of nanoparticles, physical characteristics of the polymer matrix and interphase region, dispersion quality, etc. may substantially affect the percolation threshold and subsequently other physical properties of the system, such as electrical conductivity.Highlights A structural approach toward understanding the percolation phenomenon. Defining the particular dependence of percolation threshold on dispersion quality. Estimating the content of aggregates/agglomerates using percolation theory. Improving the percolation theory to predict the electrical conductivity. Applying the scaling theory to define the electrical conductivity of the interphase.

Easy reversible clustering of gold nanoparticles via pH-Induced assembly of PVP-b-PAA copolymer

The growing demand of novel hybrid organic/inorganic systems with exciting properties has contributed to an increasing need for simplifying production strategies. Here, we report a simple method to obtain controlled three-dimensional hybrid architectures, in particular hybrid supracolloids (hSC), formed by gold nanoparticles and a double hydrophilic block copolymer, specifically the poly(acrylic acid)-block-poly(N-vinyl-2-pyrrolidone) (PAA-b-PVP), directly in aqueous medium. The ubiquitous pH-sensitive poly(acrylic acid) (PAA) block initiates the assembly through pH changes, while the poly(N-vinyl-2-pyrrolidone) block assures the close affinity with the AuNPs. We demonstrate that the formation of hybrid supracolloids (hSC) is the result of the synergetic behavior of the two specific polymeric blocks. Additionally, the entire process shows spontaneous and fast switchability. The nanostructured copolymer behaves like a highly swollen hydrogel and displays a disordered internal structure. The driving force for the association of the copolymer chains is induced by the synergetic effects of the decrease in solubility of the poly(acrylic acid) block and the formation of inter and intra chains hydrogen bonds. These were demonstrated by using small angle X-ray scattering (SAXS), quartz crystal microbalance with dissipation monitoring (QCM-D) and scanning transmission electron microscopy coupled with energy-dispersive X-ray spectroscopy (STEM-EDX). In turn, the AuNPs are randomly spread all over the polymeric matrix, as demonstrated by field emission gun – scanning electron microscopy (FEG-SEM). A correlation analysis reveals the hSC density depends mostly on the initial concentration of AuNPs. These results can inspire the fabrication of more complex structures with multicomponent composition.

Improved aggregation/agglomeration-dependent percolation theory to predict nanoparticle-aided electrical conductivity in polymer nanocomposites: A combination of analytical strategy and artificial neural network

The formation of aggregates/agglomerates in polymer nanocomposites is known as an inevitable phenomenon that may affect many physical/mechanical properties. Accordingly, in this study, the impacts of the size and content of nanoparticle clusters on the electrical conductivity of polymer matrices containing spherical nanoparticles were analytically evaluated. To this end, the well-known percolation theory was improved by involving major system parameters, affected by aggregation/agglomeration, reported to be indicative of how electricity may conduct through (e. g. tunneling effect, percolation threshold, content and equivalent electrical conductivity of nanoparticle domains, etc.). The characteristics of the aggregates/agglomerates were estimated via the combination of a devised strategy, representing the structure of clusters, and the fundamentals of the percolation theory. The average electrical conductivity and density of polymer/particle interphase, formed around nanoparticle domains were calculated using a particularly developed scaling theory. Also, the hypothetical relative location of nanoparticle domains to each other was defined by calculating the average nearest distance between them, benchmarked against the tunneling distance. A two-stage validation procedure was conducted using the data elicited from the literature in order to first, evaluate the model efficiency and secondly, assess its predictive capabilities with the aid of an artificial neural network (error < 7 %).

Comparison of different thermostat settings in the implicit solvent approach for nanoparticles through brush-decorated nanopores.

Nanoparticles (NPs) that are forcefully driven through a brush-decorated nanochannel form a nonequilibrium system with a rich physical behavior, including a dynamical phase transition between two modes of propagation that correspond to either separate clusters of NPs or a continuous flow channel. The peculiar properties of this system make it an ideal benchmark candidate for a comparison of three thermostat settings, the dissipative particle dynamics (DPD), the Langevin (LGV)dynamics, and a modified LGV setup, denoted as LGV^{-}, in which the thermostatting is disabled in the direction of the driving force. We demonstrate that the choice of the thermostat has little influence on the conformations of NPs, and that, due to differences in the dissipation modes, notable differences arise in their dynamical properties, such as effective friction constants and average velocities. This also includes differences in the coupling between NP clusters and the surrounding brush and affects the corresponding phase diagrams of the two propagation modes. We conclude that the conventional DPD and LGV thermostats yield results that display a reasonable degree of coincidence, but we cannot recommend the use of the LGV^{-} algorithm for the present system.

Assessment of photocatalytic activities of layered double hydroxide@petrochemical sludge biochar for sulfamethoxazole degradation

Layered double hydroxides (LDHs) as one of the popular photocatalysts have the drawbacks of compact structural arrangement and pronounced nano-particle clustering, which affect their photocatalytic performance for the degradation of organic pollutants from water or wastewater. Using biochar as a substrate matrix to support LDHs may be a good way to improve the photocatalytic performance of LDHs by avoiding agglomeration effect, promoting the separation of photogenerated electrons from holes for organics degradation. In this study, the composite of CoFe-LDH and petrochemical sludge biochar (CoFe-LDH@PBC) was fabricated and applied for the first time as photocatalyst for catalytic degradation of sulfamethoxazole (SMX). Results showed CoFe-LDH@PBC800/UV system exhibited excellent photocatalytic activity on SMX degradation than single CoFe-LDH/UV, PBC800/UV, or PBC105/UV system under various reaction conditions (photocatalyst dosage, pH, system speed, and SMX concentration). 100 % of SMX degradation efficiency and 88.98 % mineralization efficiency could be achieved in CoFe-LDH@PBC800/UV system within 100 min. The corresponded pseudo-first-order kinetic rate constant (kobs) of SMX degradation in CoFe-LDH@PBC800/UV system was 0.032 min−1, which was 3.20, 8.00, and 2.46 times higher than that of CoFe-LDH/UV, PBC800/UV, and PBC105/UV, respectively. In photocatalytic process, OH, O2−, and h+ participated in SMX degradation, among which h+ or OH played a major role. The good photocatalytic performance of CoFe-LDH@PBC800 composite was related to its excellent physiochemical properties, such as lower band gap (2.39 eV), high graphitization degree, and high conductivity, enabling efficient e−/h+ separation of photocatalyst for SMX degradation. Four distinct pathways of SMX degradation were proposed based on DFT theoretical calculations and byproducts of SMX degradation identified by LC-MS. Additionally, results from recycling tests suggested CoFe-LDH@PBC800 composite possessed a favorable reusability and could be applied for photocatalytic degradation of SMX at least 3 times. Over all, CoFe-LDH@PBC800 composite was proved to be an efficient photocatalyst for antibiotic degradation in water or wastewater and the reuse of petrochemical wastewater sludge was expected to realize the concept of resourceful and high-value utilization of waste biomass.

Hybrid lipid-AuNP clusters as highly efficient SERS substrates for biomedical applications

Although Surface Enhanced Raman Scattering (SERS) is widely applied for ultrasensitive diagnostics and imaging, its potential is largely limited by the difficult preparation of SERS tags, typically metallic nanoparticles (NPs) functionalized with Raman-active molecules (RRs), whose production often involves complex synthetic approaches, low colloidal stability and poor reproducibility. Here, we introduce LipoGold Tags, a simple platform where gold NPs (AuNPs) clusters form via self-assembly on lipid vesicle. RRs embedded in the lipid bilayer experience enhanced electromagnetic field, significantly increasing their Raman signals. We modulate RRs and lipid vesicle concentrations to achieve optimal SERS enhancement and we provide robust structural characterization. We further demonstrate the versatility of LipoGold Tags by functionalizing them with biomolecular probes, including antibodies. As proof of concept, we successfully detect intracellular GM1 alterations, distinguishing healthy donors from patients with infantile GM1 gangliosidosis, showcasing LipoGold Tags as advancement in SERS probes production.

Cooperative aggregation of gold nanoparticles on phospholipid vesicles is electrostatically driven.

Gold nanoparticles (AuNP) are known to aggregate on the surface of lipid vesicles, yet the molecular mechanism behind this phenomenom remains unclear. In this work, we have investigated the binding behaviour of AuNPs, synthesized with pulsed laser ablation, to phospholipid vesicles under varying conditions of ionic strength (KCl concentration) and NP to vesicle ratios. Our observations reveal a strong influence of electrolyte concentration on AuNP aggregation mediated by vesicles. Notably, cluster formation is observed even at less than one AuNP per vesicle ratio at low enough ionic strengths. These results evidence a binding mechanism governed by electrostatic attraction with a distinct cooperative behaviour at very low salt concentrations, resulting in a significant increase in nanoparticle clustering. This behaviour is quantitatively analysed through a model that incorporates the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, considering the electrical double layer attraction between dissimilar, non-oppositely charged objects. This study not only provides insight into the fundamental understanding of nanoparticle-vesicle interactions but also suggests potential strategies for controlling nanoparticle assembly in biological and synthetic systems by tuning the ionic strength.

Colloidal dispersions of cobalt ferrite nanoparticles in EMIM TFSI, propylene carbonate and their mixtures

Liquid thermocells are promising devices for the recovery of low-grade waste heat and its conversion into electricity. The present study proposes dispersing charged magnetic nanoparticles in liquids to improve thermoelectric conversion, due to their thermodiffusive and magnetic properties. Core@shell cobalt ferrite@maghemite nanoparticles (NPs) of various sizes with three different kinds of coatings are tested for dispersion in EMIM TFSI (ethyl-methylimidazolium bistriflimide), an ionic liquid (IL) of high electrical conductivity. Among the tested ligands, PAC6MIM±Br− (1-(6-hexylphosphonate) 3-methyl imidazolium bromide) emerges as the most efficient due to its phosphonic group. This ligand leads to dispersions of clusters of a few dozen NPs in water and a few NPs in EMIM TFSI. For improving both the viscosity and the electrical conductivity, dispersions in binary mixtures of propylene carbonate (PC), a polar medium, and EMIM-TFSI are further investigated with the sample based on NPs of ∼ 9 nm diameter. Through a pumping and heating procedure, stable dispersions are obtained with a subsequent reduction of the size of the NP clusters, down to 1 to 2 NPs in EMIM TFSI and a few NPs in PC and their binary mixtures. The mixture with an IL mole fraction ∼0.2–0.3 and maximal electrical conductivity is a promising candidate for further thermoelectric investigations.

Analysis of impulse withstand voltage of ester-based nanofluids

Ester-based nanofluids constitute an effective ecological alternative to petroleum-derived dielectric fluids for electrical engineering industry. Pulse induced dielectric breakdown is a key cause of power device failure. In this paper, we deal with the lightning impulse breakdown voltage (LI BDV) in natural and synthetic ester-based (NE and SE) nanofluids with various concentrations of magnetite (Fe3O4) and fullerene (C60) nanoparticles. The nanofluids show a decrement in positive polarity LI+ BDV as compared with the base ester. Negative polarity LI- BDV in the nanofluids is similar to that of base oils. Weibull statistical distributions are applied to analyse the results at different cumulative probabilities of 1 %, 10 %, and 50 %. Natural ester with C60 nanoparticles shows higher positive LI+ BDV values at a low probability level, while the negative LI- BDV reaches higher values at higher probability levels. The decreased LI BDV in NE and SE due to the magnetite nanoparticles are attributed to the nanoparticle clustering and percolation. The tendency of C60 nanoparticles to enhance the LI BDV in NE but not in SE is interpreted in the view of the nanoparticle concentration and oil's viscosity effect on the streamer branching and propagation.

Design and simulation of bifacial perovskite solar cell with high efficiency using cubic plasmonic nanoparticles

Bifacial perovskite solar cells (Bi-PSCs) are gaining attention for their ability to exceed the efficiency limits of traditional single-junction cells by absorbing light from both the front and rear surfaces. This study explores a novel approach to boost the efficiency and stability of Bi-PSCs by integrating clusters of cubic metallic nanoparticles (NPs) within the absorber layer. These plasmonic NPs, embedded in the middle of the absorber layer, significantly enhance light absorption, resulting in substantial improvements in power conversion efficiency for both front and rear irradiation. Our three-dimensional simulations demonstrate that incorporating Ag-based NPs can achieve efficiency enhancements of 29.89 % (from 18 % to 23.39 %) for front irradiation and 29.63 % (from 17.22 % to 22.33 %) for rear irradiation. Ag-based NPs exhibit the most notable efficiency improvement, reaching a short-circuit current of 41.30 mA/cm2 and an efficiency of 35.34 % at an albedo of 0.5. To address potential issues such as corrosion and degradation of the perovskite material, the metallic NPs are encapsulated with a thin dielectric nano-shell of SiO₂. This encapsulation strategy effectively prevents degradation, ensuring the long-term stability of the Bi-PSCs. The results indicate that the use of clusters of cubic metallic NPs, combined with dielectric encapsulation, offers a superior approach to optimizing the performance and stability of Bi-PSCs, providing a more efficient alternative to increasing the absorber layer’s thickness.

Active Surface-Enhanced Raman Spectroscopy (SERS): A Novel Concept for Enhancing Signal Contrast in Complex Matrices Using External Perturbation.

Noninvasive detection of surface-enhanced Raman spectroscopy (SERS) signals from deep within tissue represents a common challenge in many biological and clinical applications including disease diagnosis and therapy monitoring. Such signals are typically weak and not readily discernible from often much larger Raman and fluorescence background signals (e.g., from surrounding tissue). Consequently, suboptimal sensitivity in the detection of SERS signals is often achieved in these situations. Similar issues can arise in SERS measurements in other diffusely scattering samples and complex matrices. Here, we propose a novel concept, active SERS, for the efficient retrieval of SERS signals from deep within complex matrices such as biological tissues that mitigates these issues. It relies on applying an external perturbation to the sample to alter the SERS signal from nanoparticles (NPs) deep inside the matrix. A measurement with and without, or before and after, such perturbation then can provide powerful contrasting data enabling an effective elimination of the matrix signals to reveal more clearly the desired SERS signal without the interfering background and associated artifacts. The concept is demonstrated using ultrasound (US) as an external source of perturbation and SERS NPs inserted deep within a heterogeneous tissue phantom mimicking a cluster of NPs accumulated within a small target lesion. The overall SERS signal intensity induced by the applied US perturbation decreased by ∼21% and the SERS signal contrast was considerably improved by eliminating subtraction artifacts present in a conventional measurement performed at a neighboring spatial location in a heterogeneous tissue sample. Although the technique was demonstrated with SERS gold NPs with a standard Raman label, it is envisaged that active SERS NPs (both the nanoscale metal geometry and Raman label) could be specifically designed to deliver an augmented response to the external stimulus to further enhance the achievable SERS signal contrast and yield even greater improvement in detection sensitivity. The method was demonstrated using transmission Raman spectroscopy; however, it is also applicable to other Raman implementations including spatially offset Raman spectroscopy and conventional Raman spectroscopy performed both at depth and at surfaces of complex matrices.

Enhancing carbon mineralization of slag leachate using amine-functionalized magnetic nanoparticle clusters

Carbon mineralization—converting carbon dioxide (CO2) into stable carbonate minerals, such as calcium carbonate (CaCO3)—has attracted much attention as a permanent way to sequester CO2. In this study, we developed a novel method for improving the efficiency of carbon mineralization using amine-functionalized magnetic nanoparticle clusters (A-MNCs). To prepare them, magnetic nanoparticle clusters composed of tens of small Fe3O4 nanoparticles were treated with 3-aminopropyltriethoxysilane to functionalize amine groups on their surfaces. When CO2 was bubbled into a NaOH solution, the rate of CO2 dissolution was enhanced in the presence of A-MNCs, mostly due to physical effects such as grazing and hydrodynamic effects that improved mass transfer between the solution and CO2 bubbles. In addition, A-MNCs promoted carbon mineralization in a NaOH solution containing Ca2+ by providing an additional mineralization route via carbamate formation, resulting in a threefold increase in efficiency to 81 %. We demonstrated that A-MNCs could be applied to leachate from slag, a byproduct of commercial steelmaking, increasing the efficiency by 2.6 times compared to the absence of A-MNCs. The resulting CaCO3 precipitate was confirmed to be calcite using X-ray diffraction and scanning electron microscopy. Furthermore, after the mineralization, A-MNCs were easily separated from the solution using a permanent magnet, while retaining their performance.