A comprehensive shape analysis pipeline for stereoscopic measurements of particulate populations in suspension

Deconstructing 3D growth rates from transmission microscopy images of facetted crystals as captured in situ within supersaturated aqueous solutions

Here, a morphologically based approach is used for the in situ characterization of 3D growth rates of facetted crystals from the solution phase. Crystal images of single crystals of the β-form of l-glutamic acid are captured in situ during their growth at a relative supersaturation of 1.05 using transmission optical microscopy. The crystal growth rates estimated for both the {101} capping and {021} prismatic faces through image processing are consistent with those determined using reflection light mode [Jiang, Ma, Hazlehurst, Ilett, Jackson, Hogg & Roberts (2024 ▸). Cryst. Growth Des. 24, 3277-3288]. The growth rate in the {010} face is, for the first time, estimated from the shadow widths of the {021} prismatic faces and found to be typically about half that of the {021} prismatic faces. Analysis of the 3D shape during growth reveals that the initial needle-like crystal morphology develops during the growth process to become more tabular, associated with the Zingg factor evolving from 2.9 to 1.7 (>1). The change in relative solution supersaturation during the growth process is estimated from calculations of the crystal volume, offering an alternative approach to determine this dynamically from visual observations.

Process analytical technology in Downstream-Processing of Drug Substances- A review

Process Analytical Technology (PAT) has revolutionized pharmaceutical manufacturing by providing real-time monitoring and control capabilities throughout the production process. This review paper comprehensively examines the application of PAT methodologies specifically in the production of solid active pharmaceutical ingredients (APIs). Beginning with an overview of PAT principles and objectives, the paper explores the integration of advanced analytical techniques such as spectroscopy, imaging modalities and others into solid API substance production processes. Novel developments in in-line monitoring at academic level are also discussed. Emphasis is placed on the role of PAT in ensuring product quality, consistency, and compliance with regulatory requirements. Examples from existing literature illustrate the practical implementation of PAT in solid API substance production, including work-up, crystallization, filtration, and drying processes. The review addresses the quality and reliability of the measurement technologies, aspects of process implementation and handling, the integration of data treatment algorithms and current challenges. Overall, this review provides valuable insights into the transformative impact of PAT on enhancing pharmaceutical manufacturing processes for solid API substances.

Quid Pro Flow

How do you get into flow? We trained in flow chemistry during postdoctoral research and are now applying it in new areas: materials chemistry, crystallization, and supramolecular synthesis. Typically, when researchers think of "flow", they are considering predominantly liquid-based organic synthesis; application to other disciplines comes with its own challenges. In this Perspective, we highlight why we use and champion flow technologies in our fields, summarize some of the questions we encounter when discussing entry into flow research, and suggest steps to make the transition into the field, emphasizing that communication and collaboration between disciplines is key.

Online 3D Characterization of Micrometer-Sized Cuboidal Particles in Suspension

Characterization of particle size and shape is central to the study of particulate matter in its broadest sense. Whilst 1D characterization defines the state of the art, the development of 2D and 3D characterization methods has attracted increasing attention, due to a common need to measure particle shape alongside size. Herein, ensembles of micrometer-sized cuboidal particles are studied, for which reliable sizing techniques are currently missing. Such particles must be characterized using three orthogonal dimensions to completely describe their size and shape. To this end, the utility of an online and in-flow multiprojection imaging tool coupled with machine learning is experimentally assessed. Central to this activity, a methodology is outlined to produce micrometer-sized, non-spherical analytical standards. Such analytical standards are fabricated using photolithography, and consist of monodisperse micro-cuboidal particles of user-defined size and shape. The aforementioned activities are addressed through an experimental framework that fabricates analytical standards and subsequently uses them to validate the performance of our multiprojection imaging tool. Significantly, it is shown that the same set of data collected for particle sizing can also be used to estimate particle orientation in flow, thus defining a rapid and robust protocol to investigate the behavior of dilute particle-laden flows.

Online Monitoring of the Concentrations of Amorphous and Crystalline Mesoscopic Species Present in Solution

Despite the growing evidence for the existence of amorphous mesoscopic species in a solution and their crucial roles in crystallization, there has been the lack of a suitable method to measure the time-resolved concentrations of amorphous and crystalline mesospecies in a lab-scale stirred reactor. This has limited experimental investigations to understand the kinetics of amorphous and crystalline mesospecies formation in stirred solutions and made it challenging to measure the crystal nucleation rate directly. Here, we used depolarized light sheet microscopy to achieve time-resolved measurements of amorphous and crystalline mesospecies concentrations in solutions at varying temperatures. After demonstrating that the concentration measurement method is reasonably accurate, precise, and sensitive, we utilized this method to examine mesospecies formation both in a mixture of two miscible liquids and in an undersaturated solution of dl-valine, thus revealing the importance of a temperature change in the formation of metastable and amorphous mesospecies as well as the reproducibility of the measurements. Moreover, we used the presented method to monitor both mesospecies formation and crystal nucleation in dl-valine solutions at four different levels of supersaturation, while achieving the direct measurement of the crystal nucleation rates in stirred solutions. Our results show that, as expected, the inherent variability in nucleation originating from its stochastic nature reduces with increasing supersaturation, and the dependence of the measured nucleation rate on supersaturation is in reasonable agreement with that predicted by the classical nucleation theory.

Direct Image Feature Extraction and Multivariate Analysis for Crystallization Process Characterization

Small-scale crystallization experiments (1-8 mL) are widely used during early-stage crystallization process development to obtain initial information on solubility, metastable zone width, as well as attainable nucleation and/or growth kinetics in a material-efficient manner. Digital imaging is used to monitor these experiments either providing qualitative information or for object detection coupled with size and shape characterization. In this study, a novel approach for the routine characterization of image data from such crystallization experiments is presented employing methodologies for direct image feature extraction. A total of 80 image features were extracted based on simple image statistics, histogram parametrization, and a series of targeted image transformations to assess local grayscale characteristics. These features were utilized for applications of clear/cloud point detection and crystal suspension density prediction. Compared to commonly used transmission-based methods (mean absolute error 8.99 mg/mL), the image-based detection method is significantly more accurate for clear and cloud point detection with a mean absolute error of 0.42 mg/mL against a manually assessed ground truth. Extracted image features were further used as part of a partial least-squares regression (PLSR) model to successfully predict crystal suspension densities up to 40 mg/mL (R ² > 0.81, Q ² > 0.83). These quantitative measurements reliably provide crucial information on composition and kinetics for early parameter estimation and process modeling. The image analysis methodologies have a great potential to be translated to other imaging techniques for process monitoring of key physical parameters to accelerate the development and control of particle/crystallization processes.

Potential of Deep Learning Methods for Deep Level Particle Characterization in Crystallization

Crystalline particle properties, which are defined throughout the crystallization process chain, are strongly tied to the quality of the final product bringing along the need of detailed particle characterization. The most important characteristics are the size, shape and purity, which are influenced by agglomeration. Therefore, a pure size determination is often insufficient and a deep level evaluation regarding agglomerates and primary crystals bound in agglomerates is desirable as basis to increase the quality of crystalline products. We present a promising deep learning approach for particle characterization in crystallization. In an end-to-end fashion, the interactions and processing steps are minimized. Based on instance segmentation, all crystals containing single crystals, agglomerates and primary crystals in agglomerates are detected and classified with pixel-level accuracy. The deep learning approach shows superior performance to previous image analysis methods and reaches a new level of detail. In experimental studies, L-alanine is crystallized from aqueous solution. A detailed description of size and number of all particles including primary crystals is provided and characteristic measures for the level of agglomeration are given. This can lead to a better process understanding and has the potential to serve as cornerstone for kinetic studies.

A new approach to characterize firebrand showers using advanced 3D imaging techniques

A new approach to characterize airborne firebrands during Wildland-Urban Interface (WUI) fires is detailed. The approach merges the following two imaging techniques in a single field-deployable diagnostic tool: (1) 3D Particle Tracking Velocimetry (3D-PTV), for time-resolved mapping of firebrand 3D trajectories, and (2) 3D Particle Shape Reconstruction (3D-PSR), to reconstruct 3D models of individual particles following the Visual Hull principle. This tool offers for the first time the possibility to simultaneously study time-resolved firebrand fluxes and firebrand size distribution to the full extent of their three-dimensional nature within a control volume. Methodologies used in the present work are presented and their technical implementation are discussed. Validation tests to confirm proper tracking/sizing of particles are detailed. The diagnostic tool is applied to a firebrand shower artificially generated at the NIST National Fire Research Laboratory. A novel graphic representation, that incorporates both the Cumulative Particle Count (CPC, particles m-2) and Particle Number Flux (PNF, particles m-2 s-1) as relevant exposure metrics, is presented and the exposure level is compared to that of an actual outdoor fire. Size distributions obtained for airborne firebrands are compared to those achieved through ground collection and strategies to improve the particle shape reconstruction method are discussed.

Statistical Analysis and Nucleation Parameter Estimation from Nucleation Experiments in Flowing Microdroplets

We have studied the primary nucleation of adipic acid from aqueous solutions in thousands of microdroplets generated in a fully automated microfluidic setup. By varying supersaturation in solution and residence time, we were able to estimate nucleation rates and growth times, while accounting for the stochastic nature of nucleation, the variability in microdroplet volumes (which is kept below 2%, thanks to a carefully designed experimental protocol), and the uncertainty in the automated image analysis procedure. Through a thorough statistical analysis we have obtained exact expressions for the expected values and the variances of all the random variables involved, all the way to the nucleation rate and the growth time associated with each supersaturation level explored and to the model parameters appearing in the corresponding constitutive equations. We have analyzed what controls the overall uncertainty in the estimation of the physical quantities above. We have shown that the distribution of droplet volumes at the level observed here is not limiting, whereas the detection technique and the image analysis algorithm play a critical role, together with the fact that the supersaturation levels and residence times that can be reasonably explored are limited. The tools and methods presented and made available to the scientific community will help in making microfluidics-based studies of nucleation more effective.

An Alternative Approach to Estimate Solute Concentration: Exploiting the Information Embedded in the Solid Phase

The solute concentration in crystallization processes is generally estimated by observing properties of the liquid phase. Here, a novel method for online estimation of the change in the solute concentration caused by seeded batch crystallization or dissolution of a population of crystals in suspension is presented. The method is based on multiprojection imaging to track variations in the total solid volume of the population, thus enabling inference of the solute concentration through the mass conservation constraint. The solute concentration estimates obtained in this way are validated by using them to measure the solubilities of β l-glutamic acid and vanillin in water within certain temperature ranges and comparing them to literature data. The presented method shows promise in estimating the solute concentration reliably under circumstances where employing conventional techniques is challenging.