Julia Gala de Pablo

The widespread presence of nanoplastics (NPs) in the environment creates a significant and growing concern for global health, with ingestion, inhalation, and dermal contact identified as primary exposure pathways. Despite their documented presence in various environmental matrices and human tissues, robust quantitative data on NP levels remains scarce. This study addresses this critical gap by employing a novel and rapid flow cytometry technique to quantify nanoplastic concentrations in commercial waters, common beverages and infant food, with special focus in packaging influence. Pyrogen-free water was analyzed to establish the negative control for NP concentration, yielding 5.24 ± 2.02 events/µL. Ten commercial waters from natural springs in Spain and France showed NP levels ranging from 1.75 NP/µL to 67.94 NP/µL (mean: 19.90 ± 14.53 NP/µL), where three of those brands showed significantly higher NP numbers than the pyrogen-free water control. Compared to pyrogen-free water, infant formula and cereal porridge showed very low NP concentrations, with values of 10.27 ± 6.85 and 6.78 ± 2.27 events/µL, respectively, following triplicate analyses of six samples. Additional analyses comparing three similar soft drinks across different packaging (can, plastic bottle, or glass bottle) found no significant differences in NP concentration attributable to the container type. NPs, as ubiquitous contaminants, can be ingested by organisms through food and drink. Potential NP contamination in commercial water may be due to factors such as source water contamination, filtration and packaging. The presence of very low concentrations of NPs in infant foods suggests rigorous and effective quality control. Finally, the presence of NP in soft drinks was not affected by the type of packaging. Although soft drinks have higher NP levels than water, the type of packaging had no effect on the presence of NP in these soft drinks. Despite all plastic bottles being made of polyethylene terephthalate, variation in NP accumulation implies that material quality, storage condition, and substantially, water treatment and filtering processes contribute to NP contamination. This research gives evidence for widespread nanoplastic accumulation in bottled water, common beverages and infant formula and sets the stage for demanding research to further investigate sources, health effects, and development into effective quality control and preventive measures for public health.

Label‐free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence‐based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label‐free imaging flow cytometry have been developed, namely optofluidic time‐stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label‐free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four‐wave mixing (FWM) and second‐harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab‐made laser scanning SHG‐FWM microscope, and a high‐speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560–700 kb/s, where the event is defined by the passage of a cell or a cell‐like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES‐2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high‐content studies of biological functions and optimizing the evolution of highly productive cell strains.

Flow cytometry is an indispensable tool in biology and medicine for counting and analyzing cells in large heterogeneous populations. It identifies multiple characteristics of every single cell, typically via fluorescent probes that specifically bind to target molecules on the cell surface or within the cell. However, flow cytometry has a critical limitation: the color barrier. The number of chemical traits that can be simultaneously resolved is typically limited to several due to the spectral overlap between fluorescence signals from different fluorescent probes. Here we present color-scalable flow cytometry based on coherent Raman flow cytometry with Raman tags to break the color barrier. This is made possible by combining a broadband Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS) flow cytometer, resonance-enhanced cyanine-based Raman tags, and Raman-active dots (Rdots). Specifically, we synthesized 20 cyanine-based Raman tags whose Raman spectra are linearly independent in the fingerprint region (400–1600 cm⁻¹). For highly sensitive detection, we produced Rdots composed of 12 different Raman tags in polymer nanoparticles whose detection limit was as low as 12 nM for a short FT-CARS signal integration time of 420 µs. We performed multiplex flow cytometry of MCF-7 breast cancer cells stained by 12 different Rdots with a high classification accuracy of 98%. Moreover, we demonstrated a large-scale time-course analysis of endocytosis via the multiplex Raman flow cytometer. Our method can theoretically achieve flow cytometry of live cells with > 140 colors based on a single excitation laser and a single detector without increasing instrument size, cost, or complexity.

The last two decades have witnessed a dramatic growth of wearable sensor technology, mainly represented by flexible, stretchable, on‐skin electronic sensors that provide rich information of the wearer's health conditions and surroundings. A recent breakthrough in the field is the development of wearable chemical sensors based on surface‐enhanced Raman spectroscopy (SERS) that can detect molecular fingerprints universally, sensitively, and noninvasively. However, while their sensing properties are excellent, these sensors are not scalable for widespread use beyond small‐scale human health monitoring due to their cumbersome fabrication process and limited multifunctional sensing capabilities. Here, a highly scalable, wearable SERS sensor is demonstrated based on an easy‐to‐fabricate, low‐cost, ultrathin, flexible, stretchable, adhesive, and biointegratable gold nanomesh. It can be fabricated in any shape and worn on virtually any surface for label‐free, large‐scale, in situ sensing of diverse analytes from low to high concentrations (10–10⁶ × 10⁻⁹ m). To show the practical utility of the wearable SERS sensor, the sensor is tested for the detection of sweat biomarkers, drugs of abuse, and microplastics. This wearable SERS sensor represents a significant step toward the generalizability and practicality of wearable sensing technology.

Cell sorting is the workhorse of biological research and medicine. Cell sorters are commonly used to sort heterogeneous cell populations based on their intrinsic features. Raman‐activated cell sorting (RACS) has recently received considerable interest by virtue of its ability to discriminate cells by their intracellular chemical content, in a label‐free manner. However, the broad deployment of RACS beyond lab‐based demonstrations is hindered by a fundamental trade‐off between throughput and measurement bandwidth (i.e., cellular information content). Here this trade‐off is overcome and broadband RACS in the fingerprint region (300–1600 cm⁻¹) with a record high throughput of ≈50 cells per second is demonstrated. This represents a 25× throughput increase compared to previous demonstrations of broadband fingerprint‐region RACS outside the resonance Raman regime. To show the utility of the RACS, real‐time label‐free sorting of microalgal cells based on their accumulation of carotenoids and polysaccharide granules is demonstrated. These results hold promise for medical, biofuel, and bioplastic applications.

The last two decades have witnessed a dramatic growth of wearable sensor technology, mainly represented by flexible, stretchable, on-skin electronic sensors that provide rich information of the wearer's health conditions and surroundings. A recent breakthrough in the field is the development of wearable chemical sensors based on surface-enhanced Raman spectroscopy (SERS) that can detect molecular fingerprints universally, sensitively, and noninvasively. However, while their sensing properties are excellent, these sensors are not scalable for widespread use beyond small-scale human health monitoring due to their cumbersome fabrication process and limited multifunctional sensing capabilities. Here we demonstrate a highly scalable, wearable SERS sensor based on an easy-to-fabricate, low-cost, ultrathin, flexible, stretchable, adhesive, and bio-integratable gold nanomesh. It can be fabricated in any shape and worn on virtually any surface for label-free, large-scale, in-situ sensing of diverse analytes from low to high concentrations (10 nM - 1 mM). To show the practical utility of the wearable SERS sensor, we test the sensor for the detection of sweat biomarkers, drugs of abuse, and microplastics. This wearable SERS sensor represents a significant step toward the generalizability and practicality of wearable sensing technology.

We report highly sensitive Fourier-transform coherent anti-Stokes Raman scattering spectroscopy enabled by genetic algorithm (GA) pulse shaping for adaptive dispersion compensation. We show that the non-resonant four-wave mixing signal from water can be used as a fitness indicator for successful GA training. This method allows GA adaptation to sample measurement conditions and offers significantly improved performance compared to training using second-harmonic generation from a nonlinear crystal in place of the sample. Results include a 3 × improvement to peak signal-to-noise ratio for 2-propanol measurement, as well as a 10 × improvement to peak intensities from the high-throughput measurement of polystyrene microbeads under flow.