Predicting the Solubility of Amino Acids and Peptides with the SAFT-γ Mie Approach: Neutral and Charged Models.

Modeling approaches that can be used to predict accurately the solubility of amino acids and peptides are of interest for the design of new pharmaceutical processes and in the development of new peptide-based therapeutics. We investigate the capability of the SAFT-γ Mie group-contribution approach to predict the aqueous and alcohol solubility of glycine, alananine, valine, leucine, and serine and of di- and tripeptides containing these amino acids. New SAFT-γ Mie group interactions are characterized using experimental thermodynamic and phase-equilibrium data of compounds and mixtures that contain groups relevant to the amino acids and peptides, but no solubility data (except for the case of glycine).

Machining performance, economic and environmental analyses and multi-criteria optimization of electric discharge machining for SS310 alloy.

With the expansion of the manufacturing sector, it has become crucial to incorporate sustainable production methods in order to remain competitive in the market. This study focuses on addressing the needs of the manufacturing industry by conducting a sustainability analysis of electric discharge machining for SS310 alloy. The analysis explores the impact of various electrode materials, for instance, copper and brass, as well as different machining variables, including discharge current (I = 4-12 A), spark gap (SG = 6-12 mu), pulse duration (Pon = 15-45 μs), and duty cycle (DC = 75-85%) using Taguchi method.

Binary Adsorption Equilibria of Three CO+CH Mixtures on NIST Reference Zeolite Y (RM 8850) at Temperatures from 298 to 353 K and Pressures up to 3 MPa.

Adsorption equilibria of CO, CH, and their mixtures were measured on binderless pellets of NIST reference zeolite NaY (RM 8850) using a static gravimetric setup. The unary adsorption isotherms are reported at temperatures from 298 to 393 K up to a pressure of 3 MPa and compare favorably with independent results on RM 8850 powder. The competitive adsorption measurements were performed at temperatures from 298 to 353 K and up to 3 MPa for three premixed gas mixtures with CO molar feed compositions of 0.

Large-Scale Parameter Estimation for Crystal Structure Prediction. Part 1: Dataset, Methodology, and Implementation.

Crystal structure prediction (CSP) seeks to identify all thermodynamically accessible solid forms of a given compound and, crucially, to establish the relative thermodynamic stability between different polymorphs. The conventional hierarchical CSP workflow suggests that no single energy model can fulfill the needs of all stages in the workflow, and energy models across a spectrum of fidelities and computational costs are required. Hybrid /empirical force-field (HAIEFF) models have demonstrated a good balance of these two factors, but the force-field component presents a major bottleneck for model accuracy.

The seventh blind test of crystal structure prediction: structure generation methods.

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern.

RS-DFID Africa capacity-building initiative programme grant: harnessing unsteady phase-change heat exchange in high-performance concentrated solar power systems.

The Royal Society and UK Department for International Development supported a consortium of three universities across sub-Saharan Africa and Imperial College London with the aim of developing new knowledge on direct-steam-generation concentrated solar power (CSP) plants and supporting relevant capacity building across the Universities of Lagos, Mauritius and Pretoria. Key research findings from the programme include an improved flow-classification scheme for two-phase, liquid-liquid flows; testing of advanced surfaces with much-improved steady-state heat transfer performance-the commercial nanoFLUX surface showed up to 200% higher heat-transfer coefficients (HTCs) in pool boiling compared with other surfaces with R-134a/R-245fa; first-of-a-kind measurements of transient flow boiling HTCs, which were up to 30% lower in step perturbations than quasi-steady-state expectations in horizontal pipes with R-245fa; error estimation and corrections for laser-induced fluorescence (LIF) measurements, leading to the development of an adapted planar LIF technique with uncertainty <10% for local, instantaneous film thickness measurements in annular flows, and the application of such diagnostic methods to pool, falling-film and flow boiling in pipes; and predictions of an ~80% increase in the net present value of a case-study CSP plant when integrated with solid storage media.

Physicochemical Properties, Equilibrium Adsorption Performance, Manufacturability, and Stability of TIFSIX-3-Ni for Direct Air Capture of CO.

The use of adsorbents for direct air capture (DAC) of CO is regarded as a promising and essential carbon dioxide removal technology to help meet the goals outlined by the 2015 Paris Agreement. A class of adsorbents that has gained significant attention for this application is ultramicroporous metal organic frameworks (MOFs). However, the necessary data needed to facilitate process scale evaluation of these materials is not currently available.

Computer-aided design space identification for screening of protein A affinity chromatography resins.

The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best processes for the capture of mAbs. Although the screening of Protein A resins has been previously examined, process flexibility has not been considered to date.

A systems framework for investigating the roles of multiple transporters and their impact on drug resistance.

Efflux transporters are a fundamental component of both prokaryotic and eukaryotic cells, play a crucial role in maintaining cellular homeostasis, and represent a key bridge between single cell and population levels. From a biomedical perspective, they play a crucial role in drug resistance (and especially multi-drug resistance, MDR) in a range of systems spanning bacteria and human cancer cells. Typically, multiple efflux transporters are present in these cells, and the efflux transporters transport a range of substrates (with partially overlapping substrates between transporters).

Strategic Planning of a Joint SARS-CoV-2 and Influenza Vaccination Campaign in the UK.

The simultaneous administration of SARS-CoV-2 and influenza vaccines is being carried out for the first time in the UK and around the globe in order to mitigate the health, economic, and societal impacts of these respiratory tract diseases. However, a systematic approach for planning the vaccine distribution and administration aspects of the vaccination campaigns would be beneficial. This work develops a novel multi-product mixed-integer linear programming (MILP) vaccine supply chain model that can be used to plan and optimise the simultaneous distribution and administration of SARS-CoV-2 and influenza vaccines.

Modeling the Thermodynamic Properties of Saturated Lactones in Nonideal Mixtures with the SAFT-γ Mie Approach.

The prediction of the thermodynamic properties of lactones is an important challenge in the flavor, fragrance, and pharmaceutical industries. Here, we develop a predictive model of the phase behavior of binary mixtures of lactones with hydrocarbons, alcohols, ketones, esters, aromatic compounds, water, and carbon dioxide. We extend the group-parameter matrix of the statistical associating fluid theory SAFT-γ Mie group-contribution method by defining a new cyclic ester group, denoted cCOO.

A molecular density functional theory for associating fluids in 3D geometries.

A new free-energy functional is proposed for inhomogeneous associating fluids. The general formulation of Wertheim's thermodynamic perturbation theory is considered as the starting point of the derivation. We apply the hypotheses of the statistical associating fluid theory in the classical density functional theory (DFT) framework to obtain a tractable expression of the free-energy functional for inhomogeneous associating fluids.

Optimisation-based modelling for explainable lead discovery in malaria.

Background: The search for new antimalarial treatments is urgent due to growing resistance to existing therapies. The Open Source Malaria (OSM) project offers a promising starting point, having extensively screened various compounds for their effectiveness. Further analysis of the chemical space surrounding these compounds could provide the means for innovative drugs.

Simulation and Data-Driven Modeling of the Transport Properties of the Mie Fluid.

This work reports the computation and modeling of the self-diffusivity (*), shear viscosity (η*), and thermal conductivity (κ*) of the Mie fluid. The transport properties were computed using equilibrium molecular dynamics simulations for the Mie fluid with repulsive exponents (λ) ranging from 7 to 34 and at a fixed attractive exponent (λ) of 6 over the whole fluid density (ρ*) range and over a wide temperature (*) range. The computed database consists of 17,212, 14,288, and 13,099 data points for self-diffusivity, shear viscosity, and thermal conductivity, respectively.

A unified approach to dissecting biphasic responses in cell signaling.

Biphasic responses are encountered at all levels in biological systems. At the cellular level, biphasic dose-responses are widely encountered in cell signaling and post-translational modification systems and represent safeguards against overactivation or overexpression of species. In this paper, we provide a unified theoretical synthesis of biphasic responses in cell signaling systems, by assessing signaling systems ranging from basic biochemical building blocks to canonical network structures to well-characterized exemplars on one hand, and examining different types of doses on the other.

Environmental and economic potential of decentralised electrocatalytic ammonia synthesis powered by solar energy.

Intense efforts have been devoted to developing green and blue centralised Haber-Bosch processes (gHB and bHB, respectively), but the feasibility of a decentralised and more sustainable scheme has yet to be assessed. Here we reveal the conditions under which small-scale systems (NH-leaves) based on the electrocatalytic reduction of nitrogen (eNR) powered by photovoltaic energy could realise a decentralised scheme competitive in terms of environmental and economic criteria. For this purpose, we calculated energy efficiency targets worldwide, providing clear values that may guide research in the incipient eNR field.

The impact of 100% electrification of domestic heat in Great Britain.

Britain has been a global leader in reducing emissions, but little progress has been made on heat, which accounts for almost one-third of UK emissions and the largest single share is domestic heat, which is responsible for 17% of the national total. Given the UK's 2050 "Net-Zero" commitment, decarbonizing heat is becoming urgent and currently one of the main pathways involves its electrification. Here, we present a spatially explicit optimization model that investigates the implications of electrifying domestic heat on the operation of the power sector.

Uncertainty quantification for gene delivery methods: A roadmap for pDNA manufacturing from phase I clinical trials to commercialization.

The fast-growing interest in cell and gene therapy (C>) products has led to a growing demand for the production of plasmid DNA (pDNA) and viral vectors for clinical and commercial use. Manufacturers, regulators, and suppliers need to develop strategies for establishing robust and agile supply chains in the otherwise empirical field of C>. A model-based methodology that has great potential to support the wider adoption of C> is presented, by ensuring efficient timelines, scalability, and cost-effectiveness in the production of key raw materials.

Robust optimisation of combined rainwater harvesting and flood mitigation systems.

Combined large-scale rainwater harvesting (RWH) and flood mitigation systems are promising as a sustainable water management strategy in urban areas. These are multi-purpose infrastructure that not only provide a secondary, localised water resource, but can also reduce discharge and hence loads on any downstream wastewater networks if these are integrated into the wider water network. However, the performance of these systems is dependent on the specific design used for its local catchment which can vary significantly between different implementations.

Machine learning based prediction and experimental validation of arsenite and arsenate sorption on biochars.

Arsenic (As) contamination in water is a significant environmental concern with profound implications for human health. Accurate prediction of the adsorption capacity of arsenite [As(III)] and arsenate [As(V)] on biochar is vital for the reclamation and recycling of polluted water resources. However, comprehending the intricate mechanisms that govern arsenic accumulation on biochar remains a formidable challenge.

Enhancing EDM Machining Precision through Deep Cryogenically Treated Electrodes and ANN Modelling Approach.

The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles.