The dosimetry of neutron fields of therapeutic complex based on the U-120 cyclotron

In this work, the characteristics of the treat beam's dosimetry of fast neutrons of the U-120 cyclotron have been analyzed with the help of the ionizing method. The fast neutrons with 6, 3 MeV as medium energy have been obtained in coordination of deuterons with the target made of beryllium and are meant to be used for treating the patients with malignant neoplasms. The capacity of the dose in the beam of fast neutrons has been measured by ionization chambers of different types. The research has been done with the consideration of co-occurring gamma rays. The monitoring indices needed for calculation the duration of therapy session at given therapy dose have also been specified

Insight into the self-assembly of water-soluble perylene bisimide derivatives through a combined computational and experimental approach

We use a combination of computational and experimental techniques to study the self-assembly and gelation of water-soluble perylene bisimides derivatised at the imide position with an amino acid. Specifically, we study the likely structure of self-assembled aggregates of the alanine-functionalised perylene bisimide (PBI-A) and the thermodynamics of their formation using density functional theory and predict the UV-vis spectra of such aggregates using time-dependent density functional theory. We compare these predictions to experiments in which we study the evolution of the UV-Vis and NMR spectra and rheology of alkaline PBI-A solutions when gradually decreasing the pH. Based on the combined computational and experimental results, we show that PBI-A self-assembles at all pH values but that aggregates grow in size upon protonation. Hydrogel formation is driven not by aggregate growth but reduction of the aggregation surface-charge and a decrease in the colloidal stability of the aggregation with respect to agglomeration

Hydrogen evolution from water using heteroatom substituted fluorene conjugated co-polymers

The photocatalytic performance of fluorene-type polymer photocatalysts for hydrogen production from water in the presence of a sacrificial hole scavenger is significantly improved by the incorporation of heteroatoms into the...</p

Emissive Azobenzenes Delivered on a Silver Coordination Polymer

International audienceAzobenzene has become a ubiquitous component of functional molecules and polymeric materials because of the light-induced trans → cis isomerization of the diazene group. In contrast, there are very few applications utilizing azobenzene luminescence, since the excitation energy typically dissipates via nonradiative pathways. Inspired by our earlier studies with 2,2′-bis[N,N′-(2-pyridyl)methyl]diaminoazobenzene (AzoAMoP) and related compounds, we investigated a series of five aminoazobenzene derivatives and their corresponding silver complexes. Four of the aminoazobenzene ligands, which exhibit no emission under ambient conditions, form silver coordination polymers that are luminescent at room temperature. AzoAEpP (2,2′-bis[N,N′-(4-pyridyl)ethyl]diaminoazobenzene) assembles into a three-dimensional coordination polymer (AgAAEpP) that undergoes a reversible loss of emission upon the addition of metal-coordinating analytes such as pyridine. The switching behavior is consistent with the disassembly and reassembly of the coordination polymer driven by displacement of the aminoazobenzene ligands by coordinating analytes

Mapping binary copolymer property space with neural networks

The extremely large number of unique polymer compositions that can be achieved through copolymerisation makes it an attractive strategy for tuning their optoelectronic properties. However, this same attribute also makes it challenging to explore the resulting property space and understand the range of properties that can be realised. In an effort to enable the rapid exploration of this space in the case of binary copolymers, we train a neural network using a tiered data generation strategy to accurately predict the optical and electronic properties of 350 000 binary copolymers that are, in principle, synthesizable from their dihalogen monomers via Yamamoto, or Suzuki-Miyaura and Stille coupling after one-step functionalisation. By extracting general features of this property space that would otherwise be obscured in smaller datasets, we identify simple models that effectively relate the properties of these copolymers to the homopolymers of their constituent monomers, and challenge common ideas behind copolymer design. We find that binary copolymerisation does not appear to allow access to regions of the optoelectronic property space that are not already sampled by the homopolymers, although it conceptually allows for more fine-grained property control. Using the large volume of data available, we test the hypothesis that copolymerisation of 'donor' and 'acceptor' monomers can result in copolymers with a lower optical gap than their related homopolymers. Overall, despite the prevalence of this concept in the literature, we observe that this phenomenon is relatively rare, and propose conditions that greatly enhance the likelihood of its experimental realisation. Finally, through a 'topographical' analysis of the co-polymer property space, we show how this large volume of data can be used to identify dominant monomers in specific regions of property space that may be amenable to a variety of applications, such as organic photovoltaics, light emitting diodes, and thermoelectrics

Exploring and mapping chemical space with molecular assembly trees

The rule-based search of chemical space can generate an almost infinite number of molecules, but exploration of known molecules as a function of the minimum number of steps needed to build up the target graphs promises to uncover new motifs and transformations. Assembly theory is an approach to compare the intrinsic complexity and properties of molecules by the minimum number of steps needed to build up the target graphs. Here, we apply this approach to prebiotic chemistry, gene sequences, plasticizers, and opiates. This allows us to explore molecules connected to the assembly tree, rather than the entire space of molecules possible. Last, by developing a reassembly method, based on assembly trees, we found that in the case of the opiates, a new set of drug candidates could be generated that would not be accessible via conventional fragment-based drug design, thereby demonstrating how this approach might find application in drug discovery

The potential scarcity, or not, of polymeric overall water splitting photocatalysts

We perform a high-throughput virtual screening of a set of 3240 conjugated alternating binary co-polymers and homo-polymers, in which we predict their ability to drive sacrificial hydrogen evolution and overall water splitting when illuminated with visible light. We use the outcome of this screening to analyse how common the ability to drive either reaction is for conjugated polymers loaded with suitable co-catalysts, and to suggest promising (co-)monomers for polymeric overall water splitting catalysts

Accelerated Discovery of Organic Polymer Photocatalysts for Hydrogen Evolution from Water through the Integration of Experiment and Theory

Conjugated polymers are an emerging class of photocata-lysts for hydrogen production where the large breadth of potential synthetic diversity presents both an opportunity and a challenge. Here, we integrate robotic experimentation with high-throughput computation to navigate the available structure-property space. A total of 6354 co-polymers was considered computationally, followed by the synthesis and photocatalytic characterization of a sub-library of more than 170 co-polymers. This led to the discovery of new pol-ymers with sacrificial hydrogen evolution rates (HERs) of more than 6 mmol g-1 h-1. The variation in HER across the library does not correlate strongly with any single physical property but a machine learning model involving four sepa-rate properties can successfully describe up to 68% of the variation in the HER data between the different polymers. The four variables use in the model were the predicted elec-tron affinity, the predicted ionization potential, the optical gap, and the dispersibility of the polymer particles in solu-tion, as measured by optical transmittance

Electron density-based GPT for optimization and suggestion of host–guest binders

Here we present a machine learning model trained on electron density for the production of host–guest binders. These are read out as simplified molecular-input line-entry system (SMILES) format with &gt;98% accuracy, enabling a complete characterization of the molecules in two dimensions. Our model generates three-dimensional representations of the electron density and electrostatic potentials of host–guest systems using a variational autoencoder, and then utilizes these representations to optimize the generation of guests via gradient descent. Finally the guests are converted to SMILES using a transformer. The successful practical application of our model to established molecular host systems, cucurbit[n]uril and metal–organic cages, resulted in the discovery of 9 previously validated guests for CB[6] and 7 unreported guests (with association constant Ka ranging from 13.5 M−1 to 5,470 M−1) and the discovery of 4 unreported guests for [Pd214]4+ (with Ka ranging from 44 M−1 to 529 M−1)

Nitrogen Containing Linear Poly(phenylene) Derivatives for Photo-catalytic Hydrogen Evolution from Water

Here we study how the introduction of nitrogen into poly(p-phenylene) type materials affects their ability to act as hydrogen evolution photocatalysts. Direct photocatalytic water splitting is an attractive strategy for clean energy production, but understanding which material properties are important, how they interplay, and how they can be influenced through doping remains a significant challenge, especially for polymers. Using a combined experimental and computational approach, we demonstrate that introducing nitrogen in conjugated polymers results in either materials that absorb significantly more visible light but worse predicted driving force for water/sacrificial electron donor oxidation, or materials with an improved driving force that absorb relatively less visible light. The latter materials are found to be much more active and the former much less. The trade-off between properties highlights that the optimization of a single property in isolation is a poor strategy for improving the overall activity of materials