Dimitrios Tsaoulidis

This paper concerns chemical, biological and other experiments used in R&D labs, where the aim is to optimise some performance indicator by adjusting the materials and processing parameters. Such experiments require significant resources and time, motivating the use of early stopping strategies to terminate unpromising runs before completion. However, stopping an ongoing experiment based on incomplete observations carries the risk of incorrectly terminating runs that would have achieved satisfactory outcomes, a quantity we term the false stop rate (FSR). To address this, we propose Confidence-Bound Early Stopping with Sequential Calibration (CBES), a two-layer framework that employs Gaussian process regression to predict the final outcome from partial observations and combines a confidence-bound decision rule with a calibration procedure to ensure that the FSR remains below a user-specified level. We compare CBES against four baseline stopping criteria on two distinct domains: in vitro permeation testing (IVPT) for pharmaceutical formulation and LCBench for hyperparameter optimisation. The results demonstrate that CBES achieves reliable FSR control with substantial time savings. This work offers a flexible framework for experimental processes, with broad applicability in fields such as chemical engineering, biotechnology, and material science.
•A confidence-bound early stopping framework with sequential calibration (CBES) is proposed.•Gaussian process regression provides probabilistic predictions of final experimental outcomes.•Sequential calibration controls the false stop rate at a user-specified level without manual tuning.•A Hoeffding-based bound provides a formal generalisation guarantee on the false stop rate.•CBES achieves reliable false stop rate control with substantial time savings across two domains.

Formulating effective skin products requires navigating complex chemical mixtures,skin biophysical and biochemical properties and manufacturing processes, all under budgetary and time constraints. Controlling dermal permeation, a key driver of efficacy, often presents the primary development bottleneck. Conventional development methods are slow, hampered by low-throughput, variable test assays (e.g., in vitro release and permeation testing) and limited access to biologically relevant in vitro skin models. This review argues for a shift towards autonomous, assay-aware formulation design, outlining a closed-loop framework that unifies intelligent candidate generation, automated experiment selection and robust analysis across a skin-specific multi-tiered assay strategy. The foundations of barrier transport and formulation behaviour are first synthesised. Key enabling technologies are then systematically surveyed, including automation technologies (e.g., microfluidic and modular platforms), automated analytics (e.g., chromatographic pipelines, auto-sampling for diffusion cells) and artificial intelligence (e.g., hybrid mechanistic/data-driven surrogates and constraint-aware active learning). Building upon this foundation, a practical framework is discussed that foregrounds cross-tier calibration between rapid screens and pivotal assay endpoints. Its workflow centres on model generalisation, uncertainty quantification and robust system orchestration. The goal is to provide a credible path towards faster, more reproducible and acceptance criteria-aligned decisions for skin product formulation efficacy.

Formulated topical drugs (and personal care products) contain diverse and varied mixtures. The experiments for formulation design can be time-consuming, especially those for optimising the delivery of active ingredients into the skin, the so-called in vitro permeation test (IVPT). A single IVPT typically takes 24 hrs and consumes significant resources for sample collection and chemical analysis. In this study, an early decision-making algorithm (EDMA) that can terminate unpromising experiments early, thereby prioritising resources on promising ones and potentially accelerating formulation design is proposed. The algorithm relies on a flexible Gaussian process regression (GPR) model for prediction during the experiments, while the prediction uncertainty is accounted for by a statistical measure, the probability of exceedance (PoE), to guide decision-making. This algorithm was applied to maximise ibuprofen permeation from a gel-like formulation through IVPT. The results show that it is feasible to determine whether a certain formulation has the potential to achieve higher permeation before the end of experiment, leading to significant savings on time and resources.

Biodiesel production offers a promising route towards achieving net-zero emissions by 2050. However, the move away from crop-based feedstocks towards a variable waste base feed oil presents several challenges. This study addresses these challenges by using waste cooking oil (WCO) as a renewable feedstock for transesterification in a microreactor. Kinetic analysis was performed using both pseudo-homogeneous and biphasic second-order models to evaluate their suitability for describing the reaction.

Topical skin products aim to address aesthetic, protective, and/or therapeutic needs through interaction with the human epidermal system. Traditionally, formulation development relies on empirical knowledge and trial-and-error experiments. In this paper, we introduced the Bayesian optimisation method and compared it with the traditional response surface methodology (RSM) for topical drug formulation. The objective was to optimise the formulation composition of ibuprofen gel-like to achieve a maximum flux through in vitro permeation tests (IVPTs). As a model system, poloxamer 407, ethanol, and propylene glycol (PG) were selected as the key excipients, whose concentrations were optimised. Strat-M membrane, serving as a surrogate for human skin, and Franz cell diffusion were employed in IVPTs. Two sets of experiments were conducted under identical conditions for 30 h. Under the RSM approach, the optimised ibuprofen gel-like formulation was identified with a poloxamer 407: ethanol: PG ratio of 20:20:10, achieving a measured permeation flux of 11.28 ± 0.35 μg cm−2h−1. In comparison, Bayesian optimisation, after four iterations, yielded an optimised formulation with a ratio of 20.95:19.44:12.14, resulting in a permeation flux of 14.15 ± 0.77 μg cm−2h−1. These findings highlight the potential of Bayesian optimisation as an effective tool for improving topical drug formulations.

In this paper, the transesterification reaction of waste cooking oil (WCO) with methanol using KOH as catalyst to produce biodiesel was performed in a micro reactor (1 mm ID) using a cross -flow inlet configuration. The effects of different variables such as, methanol-to-oil molar ratio, temperature, catalyst concentration, and residence time on biodiesel yield, as well as the associated flow patterns during the transesterification reaction were investigated and the relationship between flow characteristics and mass transfer performance of the system was examined. The work reveals important aspects and the links between the hydrodynamic behaviour and the mass transfer performance of the intensified reactors. It was found that high yield (>90%) of biodiesel can be achieved in one stage reaction using cross -flow micro-reactors for a wide range of conditions, i.e., methanol-to-oil molar ratio: 8-14, catalyst concentration: 1.4%-1.8% w/w, temperature: 55 degrees C-60 degrees C, and residence times: 55-75 s.

•Design of micro total analytic system (μTAS) for on-line detection of radionuclides.•The identification of designs is treated as a problem of design under uncertainty.•Simulations were implemented to evaluate designs for different feed compositions.•The lowest detection limit for the overall detection system is identified. The development of reliable and fast automated methodologies to detect and identify radionuclides during the decommissioning of nuclear power plants is of paramount importance. In this regard, process flowsheeting and computational simulations are useful tools to aid the design and testing of these advanced detection technologies. We implement an optimization based design procedure for the design of continuous analysis systems based on microfluidic solvent extraction and on-line measurement to detect radionuclides in nuclear waste. The optimization of such detection systems is treated as a design under uncertainty problem. The systems are based on thermal lens microscopy as the detection instrument. We demonstrate our approach on a flowsheet for the detection of trivalent lanthanides in organic and aqueous solutions. We highlight the importance of using computer-aided optimization based procedures to design microsystems comprising several chemical operations and their coupling with the detection step. It constitutes a proof of concept and a first step towards robust optimization based modelling approaches for the design of microfluidic lab-on-a-chip platforms for the detection of radionuclides in nuclear waste.

[Display omitted] •Selective separations of U(VI)/Er(III) in HNO3 by TBP/D80 in small-scale reactors.•Evaluation of mixing zone on the extraction performance of the small-scale reactor.•CFD model to simulate reactor’s extraction performance using one experimental point.•Hydrodynamic parameters affecting mass transfer performance of small-scale reactors. In this paper, systematic studies are performed to identify the parameters that influence the selective separation of actinides from a mixture with lanthanides in small channels. In particular, the separation of dioxouranium metal ions (UO2+2) from a binary U(VI)/Er(III) mixture in a nitric acid solution by an organic TBP/kerosene (Exxsol D80) phase, relevant to spent nuclear fuel reprocessing is investigated. The effects of parameters such as TBP concentration, organic-to-aqueous phase flow rate ratio, channel size, and residence time on mass transfer are evaluated, whilst the mass transfer performance in the extraction channels is further analysed using two important hydrodynamic features, i.e. plug formation time and interfacial area to volume ratio. Circular channels with diameters from 1 to 3 mm are used to investigate the effect of scale on the mass transfer characteristics. The importance of the mixing zone on mass transfer is also evaluated. A CFD model is proposed to simulate the mass transfer during plug flow. Using only one experimental point, once the plug has been formed, the model is able to predict extraction percentage with less than 4% difference compared to the experiments.

In this paper, the effect of the scalability of small-scale devices on the separation of Co(II) from a binary Co(II)/Ni(II) mixture in a nitric acid solution by an organic Cyanex 272/TBP/kerosene (Exxsol D80) phase is studied. In particular, circular channels with diameters of 1, 2, and 3.2 mm are considered. The results were compared against those from a confined impinging-jets (CIJ) cell with a main channel diameter of 3.2 mm. The effects of total flowrate, residence time, Cyanex 272 concentration, and flowrate ratio on the mass transfer performance were investigated. It was found that at increased channel size, the throughputs were also increased but the extraction percentages remained the same. Higher extraction percentages were obtained by using the CIJ configuration at short residence times. However, for longer residence times, the mass transfer coefficients were similar and capillary channels should be preferred over the CIJ because of the ease of separation of the two phases at the end of the unit. The overall mass transfer coefficients ranged between 0.02 and 0.14 s for the capillary channels during plug flow and between 0.05 and 0.45 s for the CIJ cells during dispersed flow.

In this work, the extraction of U(VI) by tributyl phosphate (TBP) is studied in small channels of different sizes, operated in segmented flow. The variables analysed include the channel diameter (1-4 mm I.D.), mixture velocity (1.06 - 4.24 cm s(-1)), volume fraction of the continuous phase (between 0.200 and 0.500), and concentration of extractant (TBP 30% v/v in kerosene and TBP 100%). The hydrodynamic characteristics of the flow, such as plug and slug lengths, specific interfacial area, and dispersed phase holdup, were obtained experimentally using high-speed imaging, while the pressure drop was measured with a differential pressure transducer. These parameters were correlated to the studied variables. The concentration of uranium in the aqueous phase was measured with UV-vis spectroscopy, and the mass transfer coefficients were compared with the predictions of a numerical model of segmented flow developed in Comsol Multiphysics, with good agreement.