Particle Metrology Approach to Understanding How Storage Conditions Affect Long-Term Liposome Stability.

Lipid nanoparticles are a generic type of nanomaterial with broad applicability in medicine as drug delivery vehicles. Liposomes are a subtype of lipid nanoparticles and, as a therapeutic platform, can be loaded with a genetic material or pharmaceutical agents for use as drug treatments. An open question for these types of lipid nanoparticles is what factor(s) affect the long-term stability of the particles. The stability of the particle is of great interest to understand and predict the effective shelf-life and storage requirements. In this report, we detail a one-year study of liposome stability as a function of lipid composition, buffer composition/pH, and storage temperature. This was done in aqueous solution without freezing. The effect of lipid composition is shown to be a critical factor when evaluating stability of the measured particle size and number concentration. Other factors (i.e., storage temperature and buffer pH/composition) were shown to be less critical but still have some effect. The stability of these particles informs formulation and optimal storage requirements and assists with future developmental planning of a NIST liposome-based reference material. This work also highlights the complex nature of long-term soft particle storage in biopharmaceutical applications.

Common Single-Use Consumer Plastic Products Release Trillions of Sub-100 nm Nanoparticles per Liter into Water during Normal Use.

This study demonstrates that commonly used consumer products such as single-use food grade nylon bags and hot beverage cups lined with low-density polyethylene release nanometer-sized plastic particles at number densities >1012 L-1 when exposed to water. The number of particles released was a function of the initial water temperature (high temperature vs ambient) for each of the tested materials. Mean particle diameters were between 30 and 80 nm with few particles >200 nm. The number of particles released into hot water from food grade nylon was 7 times higher when compared to single-use beverage cups. On a particle number density basis, particles released into water from a single 300 mL hot beverage cup equate to one particle for every seven cells in the human body in a size range available for cellular uptake.

Plasmonic Sensing Studies of a Gas-Phase Cystic Fibrosis Marker in Moisture Laden Air.

A plasmonic sensing platform was developed as a noninvasive method to monitor gas-phase biomarkers related to cystic fibrosis (CF). The nanohole array (NHA) sensing platform is based on localized surface plasmon resonance (LSPR) and offers a rapid data acquisition capability. Among the numerous gas-phase biomarkers that can be used to assess the lung health of CF patients, acetaldehyde was selected for this investigation. Previous research with diverse types of sensing platforms, with materials ranging from metal oxides to 2-D materials, detected gas-phase acetaldehyde with the lowest detection limit at the µmol/mol (parts-per-million (ppm)) level. In contrast, this work presents a plasmonic sensing platform that can approach the nmol/mol (parts-per-billion (ppb)) level, which covers the required concentration range needed to monitor the status of lung infection and find pulmonary exacerbations. During the experimental measurements made by a spectrometer and by a smartphone, the sensing examination was initially performed in a dry air background and then with high relative humidity (RH) as an interferent, which is relevant to exhaled breath. At a room temperature of 23.1 °C, the lowest detection limit for the investigated plasmonic sensing platform under dry air and 72% RH conditions are 250 nmol/mol (ppb) and 1000 nmol/mol (ppb), respectively.

Evaluating changes to Ralstonia pickettii in high-purity water to guide selection of potential calibration materials for online water bioburden analyzers.

Online water bioburden analyzers (OWBAs) can provide real-time feedback on viable bacteria in high-purity water (HPW) systems for pharmaceutical manufacturers. To calibrate and validate OWBAs, which detect bacteria using scattered light and bacterial autofluorescence, standards are needed that mimic the characteristics of bacteria in HPW. To guide selection of potential standards, e.g., fluorescent microspheres, a relevant bacterial contaminant, Ralstonia pickettii, was characterized for size, count, viability, and autofluorescence after exposure for 24 h to HPW or a nutrient environment. The cells exposed to HPW showed smaller sizes, with lower counts and autofluorescence intensities, but similar spectral features. The cell characteristics are discussed in comparison with a set of fluorescent microspheres, considering factors relevant to OWBAs. These studies suggest that fluorescent microspheres should be relatively small (< 1 µm diameter) and dim, while covering a broad emission range from ≈ (420 to 600) nm to best mimic the representative R. pickettii.

The I/O transform of a chemical sensor.

A number of sensing technologies, using a variety of transduction principles, have been proposed for non-invasive chemical sensing. A fundamental problem common to all these sensing technologies is determining what features of the transducer's signal constitute a chemical fingerprint that allows for precise analyte recognition. Of particular importance is the need to extract features that are robust with respect to the sensor's age or stimulus intensity. Here, using pulsed stimulus delivery, we show that a sensor's operation can be modeled as a linear input-output (I/O) transform. The I/O transform is unique for each analyte and can be used to precisely predict a temperature-programmed chemiresistor's response to the analyte given the recent stimulus history (i.e. state of an analyte delivery valve being open or closed). We show that the analyte specific I/O transforms are to a certain degree stimulus intensity invariant and can remain consistent even when the sensor has undergone considerable aging. Significantly, the I/O transforms for a given analyte are highly conserved across sensors of equal manufacture, thereby allowing training data obtained from one sensor to be used for recognition of the same set of chemical species with another sensor. Hence, this proposed approach facilitates decoupling of the signal processing algorithms from the chemical transducer, a key advance necessary for achieving long-term, non-invasive chemical sensing.

Demonstration of fast and accurate discrimination and quantification of chemically similar species utilizing a single cross-selective chemiresistor.

Performance characteristics of gas-phase microsensors will determine the ultimate utility of these devices for a wide range of chemical monitoring applications. Commonly employed chemiresistor elements are quite sensitive to selected analytes, and relatively new methods have increased the selectivity to specific compounds, even in the presence of interfering species. Here, we have focused on determining whether purposefully driven temperature modulation can produce faster sensor-response characteristics, which could enable measurements for a broader range of applications involving dynamic compositional analysis. We investigated the response speed of a single chemiresitive In2O3 microhotplate sensor to four analytes (methanol, ethanol, acetone, 2-butanone) by systematically varying the oscillating frequency (semicycle periods of 20-120 ms) of a bilevel temperature cycle applied to the sensing element. It was determined that the fastest response (≈ 9 s), as indicated by a 98% signal-change metric, occurred for a period of 30 ms and that responses under such modulation were dramatically faster than for isothermal operation of the same device (>300 s). Rapid modulation between 150 and 450 °C exerts kinetic control over transient processes, including adsorption, desorption, diffusion, and reaction phenomena, which are important for charge transfer occurring in transduction processes and the observed response times. We also demonstrate that the fastest operation is accompanied by excellent discrimination within a challenging 16-category recognition problem (consisting of the four analytes at four separate concentrations). This critical finding demonstrates that both speed and high discriminatory capabilities can be realized through temperature modulation.

Machine learning applied to chemical analysis: sensing multiple biomarkers in simulated breath using a temperature-pulsed electronic-nose.

Monitoring of chemical species in breath offers an approach for the detection of disease and other conditions that cause homeostatic imbalance. Here, we demonstrate the use of microsensor-based devices for detecting select biomarkers in simulated exhaled breath as a step toward enabling fast and inexpensive breath-screening technology. Microhotplate elements functionalized with three chemiresistive metal-oxide films (SnO(2), In(2)O(3), and CuO) were used to acquire data in simulated breath containing single targets [(5 to 20) µmol/mol ammonia, methanol, and acetone], as well as mixtures of those species. All devices were operated with programmed thermal cycles featuring rapid temperature excursions, during which film resistances were measured. Material-specific temperature programs were optimized to achieve temperature-dependent metal-oxide sensing film conductance levels and target selectivity. A supervised hierarchical machine-learning algorithm using linear discriminant analysis for dimensional reduction of sensing data and discrimination was developed. This algorithm was employed in the classification and quantification of biomarkers. This approach to microsensor data collection and processing was successful in classifying and quantifying the model biomarkers in validation-set mixtures.

Deep Learning Image Analysis of Nanoplasmonic Sensors: Toward Medical Breath Monitoring.

Sensing biomarkers in exhaled breath offers a potentially portable, cost-effective, and noninvasive strategy for disease diagnosis screening and monitoring, while high sensitivity, wide sensing range, and target specificity are critical challenges. We demonstrate a deep learning-assisted plasmonic sensing platform that can detect and quantify gas-phase biomarkers in breath-related backgrounds of varying complexity. The sensing interface consisted of Au/SiO2 nanopillars covered with a 15 nm metal-organic framework. A small camera was utilized to capture the plasmonic sensing responses as images, which were subjected to deep learning signal processing. The approach has been demonstrated at a classification accuracy of 95 to 98% for the diabetic ketosis marker acetone within a concentration range of 0.5-80 µmol/mol. The reported work provides a thorough exploration of single-sensor capabilities and sets the basis for more advanced utilization of artificial intelligence in sensing applications.

Number Concentration Measurements of Polystyrene Submicrometer Particles.

The number of techniques to measure number concentrations and size distributions of submicrometer particles has recently increased. Submicrometer particle standards are needed to improve the accuracy and reproducibility of these techniques. The number concentrations of fluorescently labeled polystyrene submicrometer sphere suspensions with nominal 100 nm, 200 nm and 500 nm diameters were measured using seven different techniques. Diameter values were also measured where possible. The diameter values were found to agree within 20%, but the number concentration values differed by as much as a factor of two. Accuracy and reproducibility related with the different techniques are discussed with the goal of using number concentration standards for instrument calibration. Three of the techniques were used to determine SI-traceable number concentration values, and the three independent values were averaged to give consensus values. This consensus approach is proposed as a protocol for certifying SI-traceable number concentration standards.

An Interlaboratory Comparison on the Characterization of a Sub-micrometer Polydisperse Particle Dispersion.

The measurement of polydisperse protein aggregates and particles in biotherapeutics remains a challenge, especially for particles with diameters of ≈ 1 µm and below (sub-micrometer). This paper describes an interlaboratory comparison with the goal of assessing the measurement variability for the characterization of a sub-micrometer polydisperse particle dispersion composed of five sub-populations of poly(methyl methacrylate) (PMMA) and silica beads. The study included 20 participating laboratories from industry, academia, and government, and a variety of state-of-the-art particle-counting instruments. The received datasets were organized by instrument class to enable comparison of intralaboratory and interlaboratory performance. The main findings included high variability between datasets from different laboratories, with coefficients of variation from 13 % to 189 %. Intralaboratory variability was, on average, 37 % of the interlaboratory variability for an instrument class and particle sub-population. Drop-offs at either end of the size range and poor agreement on maximum counts of particle sub-populations were noted. The mean distributions from an instrument class, however, showed the size-coverage range for that class. The study shows that a polydisperse sample can be used to assess performance capabilities of an instrument set-up (including hardware, software, and user settings) and provides guidance for the development of polydisperse reference materials.

Miniaturized nanohole array based plasmonic sensor for the detection of acetone and ethanol with insights into the kinetics of adsorptive plasmonic sensing.

The present work demonstrates development of a miniaturized plasmonic platform comprised of a Au nanohole array (NHA) on a Si/Si3N4 substrate. Plasmonic responses of the NHA platform, which is coated with Cu-benzenetricarboxylate metal organic framework (MOF), are found to be promising even towards 500 nmol mol-1 (ppb) of acetone or ethanol vapors at room temperature. The sensing characteristics are further investigated by varying the operating temperature (296 K to 318 K) of the sensor and the concentrations of vapors (500 nmol mol-1 to 320 µmol mol-1). The plasmonic responses for the sensors are correlated with the adsorption of vapors on the MOF surface and modeled in accordance to Langmuir-type adsorption. Kinetic parameters are estimated for the adsorption of fixed concentrations of acetone and ethanol vapors within the studied operating temperature range. The linear variation of characteristic response time constants with the operating temperature provides Arrhenius activation energies for the adsorption of acetone and ethanol vapors. The comparatively lower activation energy estimated for the adsorption of ethanol results in faster and more sensitive response of the sensor towards that analyte. The plasmonic sensor for the detection of nmol mol-1 level acetone and ethanol vapors at room temperature along with the kinetic correlation on plasmonic response with the adsorption of the analytes described herein offer new insights to existing reports on surface modification and plasmonic detection.

Bioinspired methodology for artificial olfaction.

Artificial olfaction is a potential tool for noninvasive chemical monitoring. Application of "electronic noses" typically involves recognition of "pretrained" chemicals, while long-term operation and generalization of training to allow chemical classification of "unknown" analytes remain challenges. The latter analytical capability is critically important, as it is unfeasible to pre-expose the sensor to every analyte it might encounter. Here, we demonstrate a biologically inspired approach where the recognition and generalization problems are decoupled and resolved in a hierarchical fashion. Analyte composition is refined in a progression from general (e.g., target is a hydrocarbon) to precise (e.g., target is ethane), using highly optimized response features for each step. We validate this approach using a MEMS-based chemiresistive microsensor array. We show that this approach, a unique departure from existing methodologies in artificial olfaction, allows the recognition module to better mitigate sensor-aging effects and to better classify unknowns, enhancing the utility of chemical sensors for real-world applications.

Using Image Attributes to Assure Accurate Particle Size and Count Using Nanoparticle Tracking Analysis.

Nanoparticle tracking analysis (NTA) obtains particle size by analysis of particle diffusion through a time series of micrographs and particle count by a count of imaged particles. The number of observed particles imaged is controlled by the scattering cross-section of the particles and by camera settings such as sensitivity and shutter speed. Appropriate camera settings are defined as those that image, track, and analyze a sufficient number of particles for statistical repeatability. Here, we test if image attributes, features captured within the image itself, can provide measurable guidelines to assess the accuracy for particle size and count measurements using NTA. The results show that particle sizing is a robust process independent of image attributes for model systems. However, particle count is sensitive to camera settings. Using open-source software analysis, it was found that a median pixel area, 4 pixels2, results in a particle concentration within 20% of the expected value. The distribution of these illuminated pixel areas can also provide clues about the polydispersity of particle solutions prior to using a particle tracking analysis. Using the median pixel area serves as an operator-independent means to assess the quality of the NTA measurement for count.

Gas Sensing with Bare and Graphene-covered Optical Nano-Antenna Structures.

The motivation behind this work is to study the gas phase chemical sensing characteristics of optical (plasmonic) nano-antennas (ONA) and graphene/graphene oxide-covered versions of these structures. ONA are devices that have their resonating frequency in the visible range. The basic principle governing the detection mechanism for ONA is refractive index sensing. The change in the concentration of the analyte results in a differing amount of adsorbate and correlated shifts in the resonance wavelength of the device. In this work, bare and graphene or graphene oxide covered ONA have been evaluated for gas sensing performance. Four different analytes (ethanol, acetone, nitrogen dioxide and toluene) were used in testing. ONA response behavior to different analytes was modified by adsorption within the graphene and graphene oxide overlayers. This work is a preliminary study to understand resonance wavelength shift caused by different analytes. Results imply that the combination of well-structured ONA functionalized by graphene-based adsorbers can give sensitive and selective sensors but baseline drift effects identified in this work must be addressed for applied measurements.

Tin oxide nanowire sensor with integrated temperature and gate control for multi-gas recognition.

The selectivity of a chemiresistive gas sensor comprising an array of single-crystalline tin oxide nanowires (NWs) is shown to be greatly enhanced by combined temperature and gate voltage modulation. This dual modulation was effected by a novel microsensor platform that consisted of a suspended nitride membrane embedded with independently addressable platinum heater and back-gate structures. The sensor was evaluated in a chemical vapor exposure test consisting of three volatile organic compound (VOC) analytes in an air background; VOC concentrations ranged from 20 µmol/mol to 80 µmol/mol. During the exposure test, the temperature and gating conditions of the NW sensor were modulated in order to induce variations in the sensor's analyte response behavior. By treating these temperature- and gate-dependent analyte response variations as an identifying "fingerprint," analyte identification was achieved using a statistical pattern recognition procedure, linear discriminant analysis (LDA). Through optimization of this pattern recognition procedure, a VOC recognition rate of 98% was obtained. An analysis of the recognition results revealed that this high recognition rate could only be achieved through the combined modulation of temperature and gate bias as compared to either parameter alone. Overall, the highly accurate VOC analyte discrimination that was achieved here confirms the selectivity benefits provided by the utilized dual modulation approach and demonstrates the suitability of miniature nanowire sensors in real-world, multi-chemical detection problems.

C- and Z-shaped coordination compounds. synthesis, structure, and spectroelectrochemistry of cis- and trans-[Re(CO)3(L)]2-2,2'-bisbenzimidizolate with L = 4-phenylpyridine, 2,4'-bipyridine, or pyridine.

A series of C- and Z-shaped complexes of the form cis- and trans-[Re(CO)3(L)]2BiBzIm, where L = 4-phenylpyridine, 2,4'-bipyridine, or pyridine and BiBzIm = 2,2'-bisbenzimidizolate, have been synthesized by the reaction of [Re(CO)4]2BiBzIm with a slight excess of L in refluxing tetrahydrofuran. Five of the six compounds have been isolated and crystallographically and electrochemically characterized. Formation of the sixth, the cis form of the [Re(CO)3(4-phenylpyridine)]2BiBzIm, is evidently inhibited by the torsional steric demands of proximal 4-phenylpyridines. The compounds are acyclic analogues of recently studied tetrarhenium molecular rectangles and are of interest, in part, because of their potential to form ligand-centered mixed-valence (LCMV) compounds upon reduction by one electron. Spectroelectrochemical measurements corroborated the formation of a LCMV version of cis-[Re(CO)3(L)]2BiBzIm but failed to uncover a ligand-based intervalence transition. Electrochemical measurements revealed isomer-dependent L/L electrostatic effects, resulting in greater mixed-valence ion comproportionation for C-shaped assemblies versus Z-shaped assemblies.

Shape-selective transport through rectangle-based molecular materials: thin-film scanning electrochemical microscopy studies.

Microporous thin films (approximately equal to 50 to 400 nm) composed of discrete, cavity-containing molecular rectangles have been prepared. The films, which contain both amorphous and microcrystalline domains, display shape-selective transport behavior. They are permeable to small molecules and to molecules that are short or narrow in at least one dimension--for example, elongated planar molecules--but are impermeable to molecules lacking a narrow dimension. However, the shape selectivity is based on transport through intramolecular rather than intermolecular cavities. By using redox-active probe molecules, rates of transport through the rectangle-based material have been extracted from electrochemical measurements. Spatially resolved measurements obtained via scanning electrochemical microscopy have permitted transport through individual microcrystals to be evaluated semiquantitatively. The measurements reveal that transport is roughly two orders of magnitude slower than observed with thin microcrystalline films of molecular squares featuring similar-sized cavities. The differences likely reflect the fact that cavities within the square-based materials, but not the rectangle-based material, align to form simple one-dimensional channels.

Standing wave enhancement of red absorbance and photocurrent in dye-sensitized titanium dioxide photoelectrodes coupled to photonic crystals.

The light harvesting efficiency of dye-sensitized photoelectrodes was enhanced by coupling a TiO(2) photonic crystal layer to a conventional film of TiO(2) nanoparticles. In addition to acting as a dielectric mirror, the inverse opal photonic crystal caused a significant change in dye absorbance which depended on the position of the stop band. Absorbance was suppressed at wavelengths shorter than the stop band maximum and was enhanced at longer wavelengths. This effect arises from the slow group velocity of light in the vicinity of the stop band, and the consequent localization of light intensity in the voids (to the blue) or in the dye-sensitized TiO(2) (to the red) portions of the photonic crystal. By coupling a photonic crystal to a film of TiO(2) nanoparticles, the short circuit photocurrent efficiency across the visible spectrum (400-750 nm) could be increased by about 26%, relative to an ordinary dye-sensitized nanocrystalline TiO(2) photoelectrode.