Machine learning to empower electrohydrodynamic processing

CNN-Optimized Electrospun TPE/PVDF Nanofiber Membranes for Enhanced Temperature and Pressure Sensing

Temperature and pressure sensors currently encounter challenges such as slow response times, large sizes, and insufficient sensitivity. To address these issues, we developed tetraphenylethylene (TPE)-doped polyvinylidene fluoride (PVDF) nanofiber membranes using electrospinning, with process parameters optimized through a convolutional neural network (CNN). We systematically analyzed the effects of PVDF concentration, spinning voltage, tip-to-collector distance, and flow rate on fiber morphology and diameter. The CNN model achieved high predictive accuracy, resulting in uniform and smooth nanofibers under optimal conditions. Incorporating TPE enhanced the hydrophobicity and mechanical properties of the nanofibers. Additionally, the fluorescent properties of the TPE-doped nanofibers remained stable under UV exposure and exhibited significant linear responses to temperature and pressure variations. The nanofibers demonstrated a temperature sensitivity of -0.976 gray value/°C and pressure sensitivity with an increase in fluorescence intensity from 537 a.u. to 649 a.u. under 600 g pressure. These findings highlight the potential of TPE-doped PVDF nanofiber membranes for advanced temperature and pressure sensing applications.

A Siamese neural network framework for glass transition recognition

A Siamese neural network, which is a deep learning technique, was applied to investigate phase transitions based on polarising microscopic textures of liquid crystals like: antiferroelectric smectic CA* phase and its glass, smectic I phase and its glass, and smectic G and its glass. It is an example of a subtle transition without significant structural changes, where textures above and below the glass transition temperature are similar. The Siamese neural network could distinguish textures of the chosen liquid crystal phases from a glass of that phase. This publication provides details of the Siamese neural network and its implementation based on three different convolutional neural networks has been tested.

Virtually Possible: Enhancing Quality Control of 3D-Printed Medicines with Machine Vision Trained on Photorealistic Images

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Optimizing Environmental Sustainability in Pharmaceutical 3D Printing through Machine Learning

3D Printing (3DP) of pharmaceuticals could drastically transform the manufacturing of medicines and facilitate the widespread availability of personalised healthcare. However, with increasing awareness of the environmental damage of manufacturing, 3DP must be eco-friendly, especially when it comes to carbon emissions. This study investigated the environmental effects of pharmaceutical 3DP. Using Design of Experiments (DoE) and Machine Learning (ML), we looked at energy use in pharmaceutical Fused Deposition Modeling (FDM). From 136 experimental runs across four common dosage forms, we identified several key parameters that contributed to energy consumption, and consequently CO2 emission. These parameters, identified by both DoE and ML, were the number of objects printed, build plate temperature, nozzle temperature, and layer height. Our analysis revealed that minimizing trial-and-error by being more efficient in R&D and reducing the build plate temperature can significantly decrease CO2 emissions. Furthermore, we demonstrated that only the ML pipeline could accurately predict CO2 emissions, suggesting ML could be a powerful tool in in the development of more sustainable manufacturing processes. The models were validated experimentally on new dosage forms of varying geometric complexities and were found to maintain high accuracy across all three dosage forms. The study underscores the potential of merging sustainability and digitalization in the pharmaceutical sector, aligning with the principles of Industry 5.0. It highlights the comparable learning traits between DoE and ML, indicating a promising pathway for wider adoption of ML in pharmaceutical manufacturing. Through focused efforts to reduce wasteful practices and optimize printing parameters, we can pave the way for a more environmentally sustainable future in pharmaceutical 3DP.

Analysis and Prediction of Electrospun Nanofiber Diameter Based on Artificial Neural Network

Electrospinning technology enables the fabrication of electrospun nanofibers with exceptional properties, which are highly influenced by their diameter. This work focuses on the electrospinning of polyacrylonitrile (PAN) to obtain PAN nanofibers under different processing conditions. The morphology and size of the resulting PAN nanofibers were characterized using scanning electron microscopy (SEM), and the corresponding diameter data were measured using Nano Measure 1.2 software. The processing conditions and corresponding nanofiber diameter data were then inputted into an artificial neural network (ANN) to establish the relationship between the electrospinning process parameters (polymer concentration, applied voltage, collecting distance, and solution flow rate), and the diameter of PAN nanofibers. The results indicate that the polymer concentration has the greatest influence on the diameter of PAN nanofibers. The developed neural network prediction model provides guidance for the preparation of PAN nanofibers with specific dimensions.

Optimized coaxial focused electrohydrodynamic jet printing of highly ordered semiconductor sub-microwire arrays for high-performance organic field-effect transistors

Patterning of semiconductor polymers is pertinent to preparing and applying organic field-effect transistors (OFETs). In this study, coaxial focused electrohydrodynamic jet printing (high resolution, high speed, and convenient) was used to pattern polymer semiconductors. The influence of the key printing parameters on the width of polymer sub-microwires was evaluated. The width decreased with increasing applied voltage, printing speed, and concentration of the polymer ink. However, the width increased gradually with increasing polymer ink flow rate. A regression analysis model of the relationship between the printing parameters and width was established. Based on a regression analysis/genetic algorithm, the optimal printing parameters were obtained and the correctness of the printing parameters was verified. The optimized printing parameters stabilized the width of the arrays to ca. 110 nm and imparted a smooth morphology. Additionally, the corresponding OFETs exhibited a high mobility of 2 cm² V^-1 s^-1, which is 5× higher than that of thin-film-based OFETs. One can conveniently obtain high-performance OFETs from ordered sub-microwire arrays fabricated by CFEJ printing.

Machine Learning and Machine Vision Accelerate 3D Printed Orodispersible Film Development

Orodispersible films (ODFs) are an attractive delivery system for a myriad of clinical applications and possess both large economical and clinical rewards. However, the manufacturing of ODFs does not adhere to contemporary paradigms of personalised, on-demand medicine, nor sustainable manufacturing. To address these shortcomings, both three-dimensional (3D) printing and machine learning (ML) were employed to provide on-demand manufacturing and quality control checks of ODFs. Direct ink writing (DIW) was able to fabricate complex ODF shapes, with thicknesses of less than 100 µm. ML algorithms were explored to classify the ODFs according to their active ingredient, by using their near-infrared (NIR) spectrums. A supervised model of linear discriminant analysis was found to provide 100% accuracy in classifying ODFs. A subsequent partial least square algorithm was applied to verify the dose, where a coefficient of determination of 0.96, 0.99 and 0.98 was obtained for ODFs of paracetamol, caffeine, and theophylline, respectively. Therefore, it was concluded that the combination of 3D printing, NIR and ML can result in a rapid production and verification of ODFs. Additionally, a machine vision tool was used to automate the in vitro testing. These collective digital technologies demonstrate the potential to automate the ODF workflow.