Understanding the nervous system: lessons from Frontiers in Neurophotonics.

The Frontiers in Neurophotonics Symposium is a biennial event that brings together neurobiologists and physicists/engineers who share interest in the development of leading-edge photonics-based approaches to understand and manipulate the nervous system, from its individual molecular components to complex networks in the intact brain. In this Community paper, we highlight several topics that have been featured at the symposium that took place in October 2022 in Québec City, Canada.

Computing Surface Reaction Rates by Adaptive Multilevel Splitting Combined with Machine Learning and Ab Initio Molecular Dynamics.

Computing accurate rate constants for catalytic events occurring at the surface of a given material represents a challenging task with multiple potential applications in chemistry. To address this question, we propose an approach based on a combination of the rare event sampling method called adaptive multilevel splitting (AMS) and ab initio molecular dynamics. The AMS method requires a one-dimensional reaction coordinate to index the progress of the transition. Identifying a good reaction coordinate is difficult, especially for high dimensional problems such as those encountered in catalysis. We probe various approaches to build reaction coordinates such as support vector machine and path collective variables. The AMS is implemented so as to communicate with a density functional theory-plane wave code. A relevant case study in catalysis, the change of conformation and the dissociation of a water molecule chemisorbed on the (100) γ-alumina surface, is used to evaluate our approach. The calculated rate constants and transition mechanisms are discussed and compared to those obtained by a conventional static approach based on the Eyring-Polanyi equation with harmonic approximation. It is revealed that the AMS method may provide rate constants that are smaller than those provided by the static approach by up to 2 orders of magnitude due to entropic effects involved in the chemisorbed water molecule.

Score Function for the Optimization of the Performance of Forward Fill/Flush Differential Flow Modulation for Comprehensive Two-Dimensional Gas Chromatography.

Modulation is the key element of the comprehensive 2D gas chromatography separation. Forward fill/flush flow modulation is cost effective, robust and suitable for analysis of a wide range of samples. Even though this modulation system is well known, studies regarding its optimization are sparse. In this work, based on hundreds of experiments involving multiple column sets and modulation conditions, an approach was proposed that permits to facilitate the choice of the forward fill/flush flow modulation parameters. A score function was developed that allows to predict the forward fill/flush flow modulation process efficiency as judged by the modulated peak shape. The score function was based on the physical rules for optimized and quantitative forward fill/flush flow modulation proposed in our previous work, which state that the sum of the fill and flush modulation distances should be close to the modulation channel length and that the ratio of the flush and fill distances should be sufficiently high for efficient channel flushing. The score function was embedded in a freely available tool in the form of a forward fill/flush flow modulation calculator, which allows the user either to quickly check the relevancy of the modulation operating conditions or to obtain a suggestion for optimal modulation parameters.

Small-angle X-ray scattering intensity of multiscale models of spheroids.

The microstructure of heterogeneous catalysts often consists of multiscale aggregates of nanoparticles, some of which are highly anisotropic. Therefore, small-angle X-ray scattering, in classical or anomalous mode, is a valuable tool to characterize this kind of material. Yet, the classical exploitation of the scattered intensities through form and structure factors or by means of Boolean models of spheres is questionable. Here, it is proposed to interpret the scattered intensities through the use of multiscale Boolean models of spheroids. The numerical procedure to compute scattered intensities of such models is given and then validated on asymptotic diluted Boolean models, and its applicability is demonstrated for the characterization of alumina catalyst supports.

Quantification and morphological characterization of microfibers emitted from textile washing.

Microplastics are a subject of growing interest as they are a potential threat for living organisms. Textile microfibers (MFs) are an important microplastics sub-group that have been reported as a major source of microplastics release into the environment. This pollution occurs mainly during the washing of synthetic garments. However, standardized methods to quantify and characterize these MFs are scarce. This study proposes a new analytical protocol to characterize these MFs in number and size by means of filtration techniques, optical and electronic microscopy and automatic image post-processing. This approach was developed and validated on effluents from washing machines produced in different conditions (5 different garments, sequential cycles, and presence or not of detergent). Among the analyzed effluents, it was found that 40 to 75% of microfibers have a length comprised between 50 and 200 µm, with average microfiber diameters ranging from 8 to 17 µm depending on the type of textile. The emission range of microfibers was estimated to be between 220,000 to 2,820,000 microfibers per kg of textile depending on the type of garment and the washing conditions. The counting method developed is adapted to a certain range of textiles, such as 100% polyester fleece jackets (PET-1), 100% smooth polyester T-shirt (PET-2) and 100% acrylic sweater (PAN), and is not affected by the presence of detergent. The proposed method of characterization of these MFs lengths can also be extrapolated to the counting of other objects that have a similar morphology to the analyzed fibers. Hence, it can be helpful to develop new testing capture technologies and, thus, contribute to the enhancement of filtering techniques of several pollutants.

Mass fractal dimension from 2D microscopy images via an aggregation model with variable compactness.

Microscopy-image analysis provides precious information on size and structure of colloidal aggregates and agglomerates. The structure of colloids is often characterized using the mass fractal dimension d f $d_f$ , which is different from the two-dimensional (2D) fractal dimension d p $d_p$ that can be computed from microscopy images. In this work, we propose to use a recent morphological aggregation model to find a relationship between 2D image fractal dimension and 3D mass fractal dimension of aggregates and agglomerates. Our case study is represented by scanning transmission electron microscopy images of boehmite colloidal suspensions. The behaviour of the computed d f $d_f$ at different acid and base concentration shows a fair agreement with the results of small angle x-ray scattering and with the literature, enabling to use the d f $d_f$ versus d p $d_p$ relationship to study the impact of the composition of the colloidal suspension on the density of colloidal aggregates and agglomerates.

Boehmite powder is often employed for the manufacturing of γ $\gamma$ -alumina catalyst carriers and absorbents. This process involves boehmite colloidal particles coagulation, the size and shape of the particles affect the porosity of the final solid. As there is a high interest in tuning the porosity via the synthesis parameters, this work is aimed at the use of microscopy analysis to evaluate the mass fractal dimension of boehmite aggregates and agglomerates formed under controlled physical chemical conditions (acid and alkaline content and Brownian motion). Since the mass fractal dimension cannot be directly computed on binary two-dimensional images, a recently developed morphological model has been used to generate three-dimensional clusters characterized by a certain mass fractal dimension d f $d_f$ , the projection of the clusters leads to the estimation on the projected fractal dimension d p $d_p$ , being related to the slope on a bi-logarithmic plot perimeter versus area. According to the morphological model, the higher the d f $d_f$ , the lower the d p $d_p$ . The relationship between d f $d_f$ and d p $d_p$ has been used to estimate the mass fractal dimension from STEM images of boehmite suspensions. The trend of d f $d_f$ with respect to the acid and alkaline content in the suspensions agrees with small angle X-ray scattering measurement and with the literature.

Advanced data preprocessing for comprehensive two-dimensional gas chromatography with vacuum ultraviolet spectroscopy detection.

Comprehensive two-dimensional gas chromatography with vacuum ultraviolet detection results in sizable data for which noise and baseline drift ought to be corrected. As the data is acquired from multiple channels, preprocessing steps have to be applied to the data from all channels while being robust and rather fast with respect to the significant size of the data. In this study, we have described advanced data preprocessing techniques for such data which were not available in the existing commercial software solutions and which were dedicated primarily to noise and baseline correction. Noise reduction was performed on both the spectral and the time dimension. For the baseline correction, a morphological approach based on iterated convolutions and rectifier operations was proposed. On the spectral dimension, much less noisy and reliable spectra were obtained. From a quantitative point of view, mentioned preprocessing steps significantly improved the signal-to-noise ratio for the analyte detection (circa six times in this study). These preprocessing methods were integrated into the plugim! platform (https://www.plugim.fr/).

Determination of vacuum ultraviolet detector response factors by hyphenation with two-dimensional comprehensive gas chromatography with flame ionization detection.

Two-dimensional comprehensive gas chromatography is an established technique, employed for the characterization of complex samples. Broadband vacuum ultraviolet absorption spectroscopy detection has recently attracted a lot of attention as it is a universal detection technique characterized by good selectivity but also ease of use and amenability to coupling with two-dimensional comprehensive gas chromatography. Vacuum ultraviolet spectroscopy is particularly interesting due to the possibility of performing spectral decomposition for species that coelute in gas chromatography analysis. This detector has quantitative capabilities, however not all species absorb vacuum ultraviolet radiation the same. Unfortunately, vacuum ultraviolet relative response factors for compounds are not always available. Methods to rapidly measure vacuum ultraviolet relative response factors and generate a large database that would allow calibration free quantitative analysis of complex mixtures are therefore of great interest. In this work, a universal methodology that permits rapid measurement of vacuum ultraviolet relative response factors is reported. It involves flow modulated two-dimensional comprehensive gas chromatography with dual vacuum ultraviolet and flame ionization detection. In this set-up, flame ionization detection is employed as a quantitative reference allowing to scale vacuum ultraviolet responses of investigated compounds. This approach was validated by flow measurements and by comparing relative response factors obtained for model compounds with literature data.

Image Analysis Method for the Characterization of Trichoderma reesei During Fermentations.

This chapter describes the use of a specific image analysis method with the plug im! software for the characterization of filamentous fungus morphology. It details an application of this method with samples obtained from a fermentation process of a Trichoderma reesei strain. This fully automated and accurate image analysis method provides quantitative and representative data for morphological and topological analyses.

Quantitative performance of forward fill/flush differential flow modulation for comprehensive two-dimensional gas chromatography.

GC × GC is an advanced separation technique allowing to achieve quantitative and qualitative characterization of complex samples. In order to perform two-dimensional separation, the system must provide suitable peak modulation which will direct short impulses of first column flow towards the second column. Forward fill/ flush differential flow modulation is a cost effective and no cryogen requiring approach which allows modulation over a wide range of analytes with very different boiling points. However, optimization of the flow modulation process can be difficult to understand and quantification performance might be compromised if the parameters of the modulation process are not properly set. Modulated peak shape can be a good indication of the efficiency of the modulation process, however it is not sufficient to guarantee good quantification. Different average velocities in the beginning and the end of the thermally programmed GC run may cause different efficiency of the modulation process in various parts of the chromatogram. The purpose of this work is to investigate quantitative performance of the forward/fill flush modulation and delineate parameters that determine the effectiveness of the modulation process and its ability to properly reflect the quantitative composition of the investigated sample.

Gas chromatography vacuum ultraviolet spectroscopy: A review.

Accelerated technological progress and increased complexity of interrogated matrices imposes a demand for fast, powerful, and resolutive analysis techniques. Gas chromatography has been for a long time a 'go-to' technique for the analysis of mixtures of volatile and semi-volatile compounds. Coupling of the several dimensions of gas chromatography separation has allowed to access a realm of improved separations in the terms of increased separation power and detection sensitivity. Especially comprehensive separations offer an insight into detailed sample composition for complex samples. Combining these advanced separation techniques with an informative detection system such as vacuum ultraviolet spectroscopy is therefore of great interest. Almost all molecules absorb the vacuum ultraviolet radiation and have distinct spectral features with compound classes exhibiting spectral signature similarities. Spectral information can be 'filtered' to extract the response in the most informative spectral ranges. Developed algorithms allow spectral mixture estimation of coeluting species. Vacuum ultraviolet detector follows Beer-Lambert law, with the possibility of calibrationless quantitation. The purpose of this article is to provide an overview of the features and specificities of gas chromatography-vacuum ultraviolet spectroscopy coupling which has gained interest since the recent introduction of a commercial vacuum ultraviolet detector. Potentials and limitations, relevant theoretical considerations, recent advances and applications are explored.

BARCHAN: Blob Alignment for Robust CHromatographic ANalysis.

Two-dimensional gas chromatography (GC×GC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and superimposed on a reference chromatogram. The templates then need to be modified when different chromatograms are recorded. In this study, we present a chromatogram and template alignment method based on peak registration called BARCHAN. Peaks are identified using a robust mathematical morphology tool. The alignment is performed by a probabilistic estimation of a rigid transformation along the first dimension, and a non-rigid transformation in the second dimension, taking into account noise, outliers and missing peaks in a fully automated way. Resulting aligned chromatograms and masks are presented on two datasets. The proposed algorithm proves to be fast and reliable. It significantly reduces the time to results for GC×GC analysis.

Use of the Distance Transform for Integration of Local Measurements: Principle and Application in Chemical Engineering.

We propose an original methodology to integrate local measurement for nontrivial object shape. The method employs the distance transform of the object and least-square fitting of numerically computed weighting functions extracted from it. The method is exemplified in the field of chemical engineering by calculating the global metal concentration in catalyst grains from uneven metal distribution profiles. Applying the methodology on synthetic profiles with the help of a very simple deposition model allows us to evaluate the accuracy of the method. For high symmetry objects such as an infinite cylinder, relative errors on global concentration are lower than 1% for well-resolved profiles. For a less symmetrical object, a tetralobe, the best estimator gives a relative error below 5% at the cost of increased measurement time. Applicability on a real case is demonstrated on an aged hydrodemetallation catalyst. Sampling of catalyst grains at the inlet and outlet of the reactor allowed conclusions concerning different reactivity for the trapped metals.

Measurement of palladium crust thickness on catalysts by optical microscopy and image analysis.

Selective hydrogenation is an important process in petrochemistry to purify feedstock for polymer synthesis. For this process, catalysts containing metallic palladium deposited with an eggshell distribution on porous alumina are usually employed. For this kind of catalyst, the activity is known to be in close relation with the thickness of the palladium crust. As palladium oxide is brown and alumina is white, the palladium distribution in a catalyst bead before the reduction step can be characterized by optical microscopy. We propose an original and automatic procedure of optical image analysis to obtain a fast and robust method to measure the mean crust thickness of a catalyst batch and the corresponding standard deviation. The approach is validated by two different methods. First, we compared the crust thickness with those obtained by electron probe microanalysis. Then, catalytic tests of four samples with varying palladium crust thicknesses were performed and confirmed the expected correlation between activity and crust thickness measured by optical microscopy coupled with image analysis.