Multiple Particle Research Articles

Experimental investigation on inter-particle settling dynamics of multiple spherical particles released side by side at intermediate Reynolds numbers

The settling of solid particles in fluid constitutes a fundamental and crucial aspect with applications spanning various natural phenomena and engineering processes, including sediment transport and wastewater treatments. This paper delves into an experimental investigation aimed at comprehending the settling dynamics and self-organization of multiple spherical particles settling side by side at intermediate Reynolds numbers. The study employs an electromagnetic release device, previously developed for controlled settling of particles under gravity, ensuring simultaneous release with zero initial rotation and velocity. This research captures settling trajectories and provides insight into the flow fields surrounding particles by utilizing particle tracking and particle image velocimetry. The experiments systematically investigate the influence of the settling patterns, the flow fields, the velocities of particles, and their dependence on Reynolds number Re (Re = 52–258), the number of particles n (n = 3–8), as well as the initial spacing between particles l0* (l0* = 0–2). The results consistently reveal a left–right symmetry about the centerline in settling patterns, flow fields, and particle rotations across all values of n, l0*, and Re. The final settling pattern exhibits distinct shapes dependent on l0*: a “V” or “M” shape for l0* < 0.2, a “concave-downward” shape for 0.2 < l0* < 2, and a “straight-line” shape for l0* ≥ 2. The lateral spread of particles increases with time, particularly pronounced with smaller l0* and larger Re, attributed to strong repulsive forces between neighboring particles. Correspondingly, the maximum of horizontal velocities reduces from outside to inside and increases with decreasing l0* and increasing Re. The inner vortices are smaller than the outer vortices, which causes the lateral spread. The vertical spread increases with n but remains insensitive to Re. The average terminal settling velocities for all particles in the array are consistently smaller than those for single particles, as a portion of kinetic energy contributes to horizontal motions.

Microstructure-Based CZE Model for Crack Initiation and Growth in CGI: Effects of Graphite-Particle Morphology and Spacing

Compacted graphite iron (CGI) is an engineering material with the potential to fill the application gap between flake- and spheroidal-graphite irons thanks to its unique microstructure and competitive price. Despite its wide use and considerable past research, its complex microstructure often leads researchers to focus on models based on representative volume elements with multiple particles, frequently overlooking the impact of individual particle shapes and interactions between the neighbouring particles on crack initiation and propagation. This study focuses on the effects of graphite morphology and spacing between inclusions on the mechanical and fracture behaviours of CGI at the microscale. In this work, 2D cohesive-zone-element-based models with different graphite morphologies and spacings were developed to investigate the mechanical behaviour as well as crack initiation and propagation. ImageJ and scanning electron microscopy were used to characterise and analyse the microstructure of CGI. In simulations, both graphite particles and metallic matrix were assumed isotropic and ductile. Cohesive zone elements (CZEs) were employed in the whole domain studied. It was found that graphite morphology had a negligible effect on interface debonding but nodular inclusions can notably enhance the stiffness of the material and effectively impede the propagation of cracks within the matrix. Besides, a small distance between graphite particles accelerates the crack growth. These results can be used to design and manufacture better metal-matrix composites.

Multipurpose Additives Toward Improving the Polymer Cold Spray Process

Polymers have proven to be challenging to cold spray, particularly with high efficiency and quality when using inexpensive nitrogen (N2) and air propellants. Helium (He), when used as a process propellant, can improve spray deposit properties but is often undesirable due to its limited availability and high cost. In this study, additives of multiple particle sizes and materials were mixed with polymer powder in an effort to improve the performance of polymer sprays using mainly N2 as a process propellant. The effects of hard-phase additives on deposit microstructure were investigated by precise ion beam polishing of deposit cross sections and subsequent electron microscope imaging. Additional metrics including the density and post-spray composition of deposits were investigated to quantify the peening effect and the amount of embedded additive. Additives, regardless of size, were observed to embed in the spray deposits. Additionally, hard-phase additives demonstrated nozzle cleaning properties that continually remove polymer fouling on the nozzle walls. Inversely, sprays with polymer powder and no additives tended to clog the nozzle throat and diverging section because of continual fouling.

A Multiobjective Diet Planning Model for Diabetic Patients in the Moroccan Health Context Using Particle Swarm Intelligence

Recommended diets have a central role to play in creating a healthy dietary environment that enables populations to adopt and maintain health-promoting dietary practices. It’s well known that foods with a low glycemic load (GL) help release a concentration of glucose in the blood, These can contribute to the prevention of various glycemia-elated health problems. We aim to address an optimization model for diets in the Moroccan context that controls both glycemic load and total meal costs. The application of a multiple objective particle swarm method which aggregates the problem’s restrictions with optimized objectives functions helps maintain dietary diversity and facilitates the search for trade-offs between objectives and problem-specific requirements.

Blast Eye Injury After Mobile Phone Battery Explosion: A Case Report

Introduction : Injuries from mobile phone blasts have been on the rise in recent years. Mobile phone blasts cause serious ocular morbidity. We report a rare case of blast eye injury after mobile phone battery explosion.
 Case Illustration : A 16 years old woman presented with burning sensation on both the eyes, following a smartphone battery blast. Immediately, after disconnecting the phone from the charger, the battery exploded and the phone blew apart. Her presenting visual acuity was 1/300 and 1.0 in right eye and left eye. Examination revealed broad corneal haziness on right eye, charred lashes, circumciliary congestion, and multiple soot particles embedded over the cornea, conjunctiva, and the lid margins. The eyes were washed with copious ringer lactate and the debris was carefully removed. Topical preservative free lubricants, broad-spectrum antibacterial agent, corticosteroid, and cycloplegic agent were prescribed along with systemic nonsteroidal anti-inflammatory drug. The healing of epithelial defects started on the next day and was complete by the 14th day.
 Discussion : We describe an ocular blast injury due to mobile phone explosion. The mechanism of injury from battery blast could be a combination of mechanical (battery pieces), thermal, and chemical injuries. The vision can be saved if the ocular surface burns are treated promptly and properly.
 Conclusion : Mobile blast injury is emerging as a new mode of ocular trauma. Knowing proper management is very important to achieve the best result.

A novel TMR sensor based on digital lock-in amplifier technology for portable oil multipollutant detection system

Metal particles identification in lubrication oil has become a popular technology to perform the fault diagnosis and health monitoring of machinery equipment. Unfortunately, the magnetic signals induced by particles are generally weak or even submerged in heavy noise. The sensitivity of conventional inductive sensors for detecting non-ferrous particles can still be improved to better distinguish between different non-ferrous particles. Based on the above issues, this paper proposed a novel tunnel magnetoresistance (TMR) sensor based on digital lock-in amplifier (DLIA) technology to detect ferrous and multiple nonferrous metallic particles in oil under noisy conditions. Moreover, compared to conventional inductive sensor structures, the proposed method can effectively reduce the sensor volume, which is particularly necessary when considering the design of portable oil multipollutant detection system. Owing to the high resolution of TMR and outstanding versatility of DLIA for extracting weak signal overwhelmed in noise, the detection sensitivity can be improved tremendously. Extensive simulation and experimental results validated that the proposed approach was practical and effective. Based on the experimental results, the minimum values of detectable diameter could be computed as 24.76 µm for iron particles, 81.34 µm and 82.32 µm for copper and aluminium particles respectively.

Study on surface formation mechanism of multiple abrasive particles under ultrasound

ABSTRACT The mechanism of material removal under ultrasonic vibration has not been elucidated, which is not conducive to its promotion and application in the field of machining. Previous research on the mechanism of material removal was mainly conducted through grinding experiments with a single abrasive particle. The interference effect of abrasive particles on the surface has been ignored, and it is obvious that this research method has shortcomings. In this study, tangential ultrasonic vibration assisted grinding (TUVAG) and radial ultrasonic vibration assisted grinding (RUVAG) experiments were conducted on multiple abrasive particles to reveal basic principles of material removal and surface formation. Three tools with multiple abrasive particle arrangements were specially designed. The debris and groove morphology were analyzed and compared with conventional grinding (CG). This research work explains the surface formation mechanism under different forms of ultrasonic vibration and has reference significance for efficient and high-quality machining of difficult-to-cut materials.

Dual Higgs modes entangled into a soliton lattice in CuTe

Recently discovered Higgs particle is a key element in the standard model of elementary particles and its analogue in materials, massive Higgs mode, has elucidated intriguing collective phenomena in a wide range of materials with spontaneous symmetry breaking such as antiferromagnets, cold atoms, superconductors, superfluids, and charge density waves (CDW). As a straightforward extension beyond the standard model, multiple Higgs particles have been considered theoretically but not yet for Higgs modes. Here, we report the real-space observations, which suggest two Higgs modes coupled together with a soliton lattice in a solid. Our scanning tunneling microscopy reveals the 1D CDW state of an anisotropic transition metal monochalcogenide crystal CuTe is composed of two distinct but degenerate CDW structures by the layer inversion symmetry broken. More importantly, the amplitudes of each CDW structure oscillate in an out-of-phase fashion to result in a regular array of alternating domains with repeating phase-shift domain walls. This unusual finding is explained by the extra degeneracy in CDWs within the standard Landau theory of the free energy. The multiple and entangled Higgs modes demonstrate how novel collective modes can emerge in systems with distinct symmetries broken simultaneously.

Spatially Uniform and Quantitative Surface-Enhanced Raman Scattering under Modal Ultrastrong Coupling Beyond Nanostructure Homogeneity Limits.

We developed a substrate that enables highly sensitive and spatially uniform surface-enhanced Raman scattering (SERS). This substrate comprises densely packed gold nanoparticles (d-AuNPs)/titanium dioxide/Au film (d-ATA). The d-ATA substrate demonstrates modal ultrastrong coupling between localized surface plasmon resonances (LSPRs) of AuNPs and Fabry-Pérot nanocavities. d-ATA exhibits a significant enhancement of the near-field intensity, resulting in a 78-fold increase in the SERS signal for crystal violet (CV) compared to that of d-AuNP/TiO2 substrates. Importantly, high sensitivity and a spatially uniform signal intensity can be obtained without precise control of the shape and arrangement of the nanoscale AuNPs, enabling quantitative SERS measurements. Additionally, SERS measurements of rhodamine 6G (R6G) on this substrate under ultralow adsorption conditions (0.6 R6G molecules/AuNP) show a spatial variation in the signal intensity within 3%. These findings suggest that the SERS signal under modal ultrastrong coupling originates from multiple plasmonic particles with quantum coherence.

DEM and DEM-CFD modeling of systems with geometric constrictions using a new particle location based multi-level coarse graining approach

A popular Lagrangian approach for modeling granular systems is the discrete element method (DEM). Currently, the simulation of industrial granular systems often exceeds modern computer capacities. Therefore, the coarse graining (CG) approach bundles multiple particles into grains, which are less computationally demanding due to the reduced number of entities considered. In systems, where the maximum grain size is limited by local geometry constrictions, uniform grain sizes lead to inefficient computations since far from the constrictions larger grain sizes could be reasonably used. Multi-level coarse graining (MCG) addresses this by using multiple grain sizes, which are adapted to the geometric constrictions. In this study, geometrical non-uniform hoppers and a Wurster coater system were modeled using a new MCG approach. Results from literature regarding hopper discharge were matched. For the Wurster coater system MCG outperforms conventional CG by providing a higher resolution of the granular material close to the geometric constriction.

All-silicon metasurface-enabled non-coaxial superposition of vector vortex beams

Vector vortex beams (VVBs) possess great applications in fundamental science, from particle trapping to quantum communication. Recently, the complex system for evaluating VVBs has been simplified by implementing metasurfaces and demonstrating the evolutionary trends of its coaxial polarization states. Nevertheless, it is quite challenging to obtain the polarization singularities generated in the terahertz (THz) band when non-coaxial superposition is performed by two multiplexed VVBs. Herein, a minimalist all-silicon metasurface platform is proposed, capable of being implemented to evaluate the non-coaxial superposition states of two identical VVBs. The employment of spin-polarization multiplexing techniques gives higher encoding degrees of freedom within the orthogonal circular polarization channel, enabling the superposition behavior of inhomogeneous polarization states to be performed at predefined focal planes. A systematic analysis of the newly generated polarization singularities in the interaction region is performed by applying the full Stokes polarization vector. With the ultra-compact configuration, the experimental results obtained by the proposed strategy under THz wave illumination exhibit good agreement with the numerical simulations. Thus, such mechanisms offer potential applications for multiple particle capture based on non-coaxial superimposition of VVB and provide efficient meta-platform in data encryption, high-resolution imaging, and quantum communications.

Investigating the temporal dynamics of sub-micron particles and particle-bound transition metals in indoor air of a metropolitan city.

The present study portrays an association between particle-bound transition metals and children's health. The indoor air quality of the urban metropolitan city households was monitored for four PM sizes, namely PM1.0-2.5, PM0.50-1.0, PM0.25-0.50 and PM<0.25, in major seasons observed in the city; summer and winter. Further transition/heavy metals, viz. Cr, Cu, Fe, Mn, Ni, Pb and Zn, were analysed in PM1-2.5 samples. In order to evaluate the effect, health risk assessment was performed using mathematical and computational model for assessing dermal exposure and dose estimation (multiple path particle dosimetry model version3.0). The study principally targeted the children aged 2-15years for the health risk assessment. According to the results, for the largest particle size i.e. PM1.0-2.5 the highest deposition was in the head region (49.1%) followed by pulmonary (43.6%) and tracheobronchial region (7.2%), whereas, for the smallest particle size i.e. PM<0.25 the highest deposition was obtained in the pulmonary region (73.0%) followed by the head (13.6%) and TB region (13.2%). Also, the most imperilled group of children with highest dose accumulation was found to be children aged 8-9years for all particle sizes. Moreover, the dermal exposure dose as evaluated was found to be preeminent for Ni, Zn and Pb. Besides, seasonal variation gesticulated towards elevated concentrations in winter relative to the summer season. Altogether, the study will provide a conception to the researchers in the fields mounting season-specific guidelines and mitigation approaches. Conclusively, the study commends future work focussing on defining the effects of other chemical components on particles and associated transition metal composition along with proper extenuation of the same.

Reduction of multiple scattering of positively charged ultra relativistic particles channeling in planar fields of single crystals

Based on the theory of multiple scattering of positively charged particles channeled in the planes of a single crystal of silicon, calculations of the rms angle of scattering have been made for particles in the range of momenta 100–7000 GeV/c. As a result of the analysis, we find that the rms angle of multiple particle scattering can be represented by in the form of (Cl)1/w/ph, where p is the particle momentum, l is the crystal thickness and C,w,h are the constant coefficients. We show that these calculation results can be represented in a unified way using the found universal function. This function is shown to be in a good agreement with the experimental data. Furthermore, our analysis is consistent with the effect (predicted by Luboshits and Podgoretsky) of reducing multiple scattering of above-barrier particles moving at small angles to the planes of the crystal. In general, the obtained results give a simple and clear description of the scattering process under consideration.

Mammalian orthoreovirus can exit cells in extracellular vesicles.

Several egress pathways have been defined for many viruses. Among these pathways, extracellular vesicles (EVs) have been shown to function as vehicles of non-lytic viral egress. EVs are heterogenous populations of membrane-bound structures released from cells as a form of intercellular communication. EV-mediated viral egress may enable immune evasion and collective viral transport. Strains of nonenveloped mammalian orthoreovirus (reovirus) differ in cell lysis phenotypes, with T3D disrupting cell membranes more efficiently than T1L. However, mechanisms of reovirus egress and the influence of transport strategy on infection are only partially understood. To elucidate reovirus egress mechanisms, we infected murine fibroblasts (L cells) and non-polarized human colon epithelial (Caco-2) cells with T1L or T3D reovirus and enriched cell culture supernatants for large EVs, medium EVs, small EVs, and free reovirus. We found that both reovirus strains exit cells in association with large and medium EVs and as free virus particles, and that EV-enriched fractions are infectious. While reovirus visually associates with large and medium EVs, only medium EVs offer protection from antibody-mediated neutralization. EV-mediated protection from neutralization is virus strain- and cell type-specific, as medium EVs enriched from L cell supernatants protect T1L and T3D, while medium EVs enriched from Caco-2 cell supernatants largely fail to protect T3D and only protect T1L efficiently. Using genetically barcoded reovirus, we provide evidence that large and medium EVs can convey multiple particles to recipient cells. Finally, T1L or T3D infection increases the release of all EV sizes from L cells. Together, these findings suggest that in addition to exiting cells as free particles, reovirus promotes egress from distinct cell types in association with large and medium EVs during lytic or non-lytic infection, a mode of exit that can mediate multiparticle infection and, in some cases, protection from antibody neutralization.

Smart Shelf System for Customer Behavior Tracking in Supermarkets.

Transactional data from point-of-sales systems may not consider customer behavior before purchasing decisions are finalized. A smart shelf system would be able to provide additional data for retail analytics. In previous works, the conventional approach has involved customers standing directly in front of products on a shelf. Data from instances where customers deviated from this convention, referred to as "cross-location", were typically omitted. However, recognizing instances of cross-location is crucial when contextualizing multi-person and multi-product tracking for real-world scenarios. The monitoring of product association with customer keypoints through RANSAC modeling and particle filtering (PACK-RMPF) is a system that addresses cross-location, consisting of twelve load cell pairs for product tracking and a single camera for customer tracking. In this study, the time series vision data underwent further processing with R-CNN and StrongSORT. An NTP server enabled the synchronization of timestamps between the weight and vision subsystems. Multiple particle filtering predicted the trajectory of each customer's centroid and wrist keypoints relative to the location of each product. RANSAC modeling was implemented on the particles to associate a customer with each event. Comparing system-generated customer-product interaction history with the shopping lists given to each participant, the system had a general average recall rate of 76.33% and 79% for cross-location instances over five runs.

Multiple local particle filter for high-dimensional system identification

Nonlinearity and high dimensionality emerge as two primary challenges in the realm of system identification within the context of structural health monitoring (SHM) applications. Particle filter (PF) has been demonstrated efficient for nonlinear identification, but it suffers from the curse of dimensionality and behaves poorly in high-dimensional problems. The idea of state and measurement partitioning has been used in many PF algorithms to simplify high-dimensional identification problems into the identification of several lower-dimensional subgroups, but with very few applications to SHM problems. In this context, by combining multiple particle filters (MPF) with the decay of correlations property, this paper develops a novel multiple local particle filter (MLPF) for high-dimensional identification problems. A whole state vector is partitioned into several state subgroups, each consisting of fewer state components and then estimated by one PF through a novel likelihood including the local state and measurement vectors. The feasibility and efficiency of the proposed method are tested through a benchmark toy example, a case study of a twenty-story Bouc-Wen frame structure under ground motion, and an experimental study of fatigue delamination growth in composites.

Prediction of concrete abrasion depth and computational design optimization of concrete mixtures

The failure of concrete for hydraulic structures due to the excessive abrasion depth has caused great economic losses. Despite the tremendous work that has been done, the durability design of hydraulic concrete structures concerning abrasion damage is still a difficult task, especially with a long service life aiming at economic benefit and environmental sustainability. In this paper, machine learning methodologies including random forests (RFs) and artificial neural networks (ANNs), are used to establish prediction models based on concrete mixture proportions, curing age, and hydraulic conditions. Furthermore, RF models are coupled with multiple objective particle swarm optimization (MOPSO) algorithms to perform computational design optimization of concrete mixtures. The optimal solutions are generated based on 5000 randomly generated mixture proportions, among which three optimal designs of concrete mixtures are selected to guide field applications on account of concrete durability requirements and economic benefits.

3D printer emissions elicit filament-specific and dose-dependent metabolic and genotoxic effects in human airway epithelial cells.

Three-dimensional (3D) printers have become popular educational tools in secondary and post-secondary STEM curriculum; however, concerns have emerged regarding inhalation exposures and associated health risks. Current evidence suggests that filament materials and site conditions may cause differences in the chemical profiles and toxicological properties of 3D printer emissions; however, few studies have evaluated exposures directly in the classroom. In this study, we monitored and sampled particulate matter (PM) emitted from acrylonitrile-butadiene-styrene (ABS) and polylactic acid (PLA) filaments during a 3-hour 3D printing session in a high school classroom using aerosol monitoring instrumentation and collection media. To evaluate potential inhalation risks, Multiple Path Particle Dosimetry (MPPD) modeling was used to estimate inhaled doses and calculate in vitro concentrations based on the observed aerosol data and specific lung and breathing characteristics. Dynamic light scattering was used to evaluate the hydrodynamic diameter, zeta potential, and polydispersity index (PDI) of extracted PM emissions dispersed in cell culture media. Small airway epithelial cells (SAEC) were employed to determine cellular viability, genotoxic, inflammatory, and metabolic responses to each emission exposure using MTS, ELISA, and high-performance liquid chromatography-mass spectrometry (HPLC-MS), respectively. Aerosol monitoring data revealed that emissions from ABS and PLA filaments generated similar PM concentrations within the ultrafine and fine ranges. However, DLS analysis showed differences in the physicochemical properties of ABS and PLA PM, where the hydrodynamic diameter of PLA PM was greater than ABS PM, which may have influenced particle deposition rates and cellular outcomes. While exposure to both ABS and PLA PM reduced cell viability and induced MDM2, an indicator of genomic instability, PLA PM alone increased gamma-H2AX, a marker of double-stranded DNA breaks. ABS and PLA emissions also increased the release of pro-inflammatory cytokines, although this did not reach significance. Furthermore, metabolic profiling via high performance liquid chromatography-mass spectrometry (HPLC-MS) and subsequent pathway analysis revealed filament and dose dependent cellular metabolic alterations. Notably, our metabolomic analysis also revealed key metabolites and pathways implicated in PM-induced oxidative stress, DNA damage, and respiratory disease that were perturbed across both tested doses for a given filament. Taken together, these findings suggest that use of ABS and PLA filaments in 3D printers within school settings may potentially contribute to adverse respiratory responses especially in vulnerable populations.

Separation of protein corona from nanoparticles under intracellular acidic conditions: effect of protonation on nanoparticle-protein and protein-protein interactions.

Protein coronas separate from nanoparticles under intracellular acidic conditions however, competitive adsorption of multiple proteins and their protein network formation under different pH conditions have not yet been systematically studied at the atomic scale. Herein, we report all-atom molecular dynamics simulations of plasma proteins (human serum albumin and immunoglobulin gamma-1 chain C) adsorbed to 10 nm-sized carboxyl-terminated polystyrene (PS) nanoparticles at different protonation states that mimic extracellular and intracellular pH conditions of 7, 6-5, and 4.5. Binding free energies are calculated from umbrella sampling simulations, showing the significantly weakened binding between PS particles and proteins at the protonation state at pH 4.5, in agreement with experiments showing the separation of protein corona from nanoparticles at pH 4.5. Mixtures of multiple proteins and PS particles are also simulated, showing much less protein adsorption and protein cluster formation at the protonation state at pH 4.5 than that at higher pH values, which are further confirmed by calculating the diffusivities and hydrodynamic radii of individual proteins. In particular, electrostatic particle-protein and protein-protein interactions are significantly weakened by a combination of particle and protein protonation rather than by particle protonation alone, to an extent dependent on different proteins. These findings help explain the experimental observations regarding separation of protein corona from nanoparticles under intracellular acidic conditions at pH 4.5 but not at higher pH, supporting that acidification cannot be the only reason for this separation during the process of endosome maturation.

Stable "snow lantern-like" aggregates of silicon nanoparticles suitable as a drug delivery platform.

Nanomedicine is a growing field where development of novel organic and inorganic materials is essential to meet the complex requirements for drug delivery. This includes biocompatibility, suitability for surface modifications, biodegradability, and stability sufficient to carry a drug payload through various tissues for the desired timespan. Porous silicon nanoparticles (pSi NP) are shown to have several beneficial traits in drug delivery in addition to a porous structure to maximize drug loading. The conventional synthesis of pSi NP using electrochemical etching is costly, time-consuming and requires large quantities of highly toxic hydrofluoric acid (HF). As such this research attempted a novel method to address these limitations. Mesoporous silicon nanoparticles were prepared by centrifugal Chemical Vapor Deposition (cCVD) without the use of HF. This process generated aggregates consisting of multiple primary particles fused into each other, similar to snowballs fused together in a snow-lantern (snowball pyramid). Our results demonstrated that the cCVD Si particles were versatile in terms of surface chemistry, colloidal stability, degradability, minimization of acute in vitro toxicity, and modulation of drug release. Dynamic light scattering, scanning electron microscopy, and cryogenic nitrogen adsorption isotherm measurements confirmed the overall size (210 nm), morphology, and pore size (14-16 nm) of the prepared materials. Agglomeration in phosphate-buffered saline (PBS) was minimized by PEGylation by a two-step grafting procedure that employed a primary amine linker. Finally, the release rate of a model drug, hydrocortisone, was evaluated with both PEGylated and pristine particles. Conclusively, these snow-lantern cCVD Si particles do indeed appear suitable for drug delivery.