Structure and Reactivity of N-Heterocyclic Alkynyl Hypervalent Iodine Reagents

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Macrocycles provide an attractive modality for drug development but the identification of ligands to targets of interest is hindered by the lack of large macrocyclic compound libraries for high-throughput screening. A strategy to efficiently synthesize lar ...

Base excision repair enzymes (BERs) detect and repair oxidative DNA damage with efficacy despite the small size of the defects and their often only minor structural impact. A charge transfer (CT) model for rapid scanning of DNA stretches has been evoked to ...

The constant urge to construct new molecules in an economical and sustainable fashion led to the development of numerous metal-catalyzed transformations. Organocatalysts consisting of abundant and more sustainable elements offer an elegant solution to over ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

Supercritical water gasification (SCWG) is a promising technology to convert wet biomass to renewable natural gas. The present study sheds light on the chemical composition and the formation mechanism of coke deposits on a Ru/CNF catalyst during continuous ...

Alkynes are found in a multitude of natural or synthetic bioactive compounds. In addition to the capacity of these chemical motifs to impact the physicochemical properties of a molecule of interest, the well-established reactivity of alkynes makes them ide ...

We present an efficient method to compute diffusion coefficients of multiparticle systems with strong interactions directly from the geometry and topology of the potential energy field of the migrating particles. The approach is tested on Li-ion diffusion ...

Macrocycles have raised much interest in the pharmaceutical industry due to their ability to bind challenging targets while often still being able to cross membranes to reach intracellular proteins. However, the development of macrocyclic ligands to new di ...

Structure determination of amorphous materials remains challenging, owing to the disorder inherent to these materials. Nuclear magnetic resonance (NMR) powder crystallography is a powerful method to determine the structure of molecular solids, but disorder ...

Bis(trifluoromethylated)benziodoxoles (Bx) are broadly used cyclic hypervalent iodine reagents due to their stability and unique chemical properties. However, current methods to access them require several steps and long reaction times, making their synthe ...

Machine learned interatomic interaction potentials have enabled efficient and accurate molecular simulations of closed systems. However, external fields, which can greatly change the chemical structure and/or reactivity, have been seldom included in curren ...

At present, there is no general standard automated method for engineering metalloenzymes, industrially-relevant systems able to catalyze environmentally friendly reactions. One of the most studied natural metalloenzymes is the second isoform of human carbo ...

High-throughput generation of large and consistent ab initio data combined with advanced machine-learning techniques are enabling the creation of interatomic potentials of near ab initio quality. This capability has the potential of dramatically impacting ...

Acoustic emission (AE) monitoring is commonly used to inspect the health of a structure continuously. During fracture processes elastic waves of AE are created and emitted, and sensors can capture these waves. The acquired signals can be processed to track ...

Over the past decade we have developed Koopmans functionals, a computationally efficient approach for predicting spectral properties with an orbital-density-dependent functional framework. These functionals impose a generalized piecewise linearity conditio ...

Computational chemistry aims to simulate reactions and molecular properties at the atomic scale, advancing the design of novel compounds and materials with economic, environmental, and societal implications. However, the field relies on approximate quantum ...

Over the past decade, interatomic potentials based on machine learning (ML) techniques have become an indispensable tool in the atomic-scale modeling of materials. Trained on energies and forces obtained from electronic-structure calculations, they inherit ...

The most promising solution towards cementitious materials with a lower carbon footprint is the partial substitution of the clinker by supplementary cementitious materials (SCMs) such as fly ash, blast furnace slag, limestone and calcined clays. The produc ...

Integrating rich cutaneous haptic feedback enhances realism and user immersion in virtual and augmented reality settings. One major challenge is providing accurately localized cutaneous stimuli on fingertips without interfering with the user's dexterity. T ...

Structure and Reactivity of N-Heterocyclic Alkynyl Hypervalent Iodine Reagents

Chat with Graph Search

Methods for high-throughput synthesis and screening of peptide libraries

Oxidative Defect Detection Within Free and Packed DNA Systems: A Quantum Mechanical/Molecular Mechanics (QM/ MM) Approach

New Applications of 1,3,2-Diazaphospholenes in Catalysis

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

The nature of carbon deposits and their formation pathways during catalytic supercritical water gasification of glycerol

Hypervalent Iodine: New Reagents and Functionalization of Peptides

Predicting Ion Diffusion from the Shape of Potential Energy Landscapes

Development of methods for the synthesis of large combinatorial libraries of macrocyclic compounds

Atomic-level structure determination of amorphous molecular solids by NMR

One-pot synthesis of functionalized bis(trifluoromethylated)benziodoxoles from iodine(i) precursors

Universal machine learning for the response of atomistic systems to external fields

Computational Studies for the Design of Metalloenzyme Mimics

Machine-learned interatomic potentials: Recent developments and prospective applications

Automatic and high-precision microseismic monitoring of progressive failure prior, during, and after tunnel excavation

koopmans: An Open-Source Package for Accurately and Efficiently Predicting Spectral Properties with Koopmans Functionals

Advancing Computational Chemistry with Stochastic and Artificial Intelligence Approaches

Beyond potentials: Integrated machine learning models for materials

The role of Zn in C-S-H atomic structure, nucleation and growth

Fully 3D-Printed, Stretchable, and Conformable Haptic Interfaces