ChemRxiv
Not a member yet
27047 research outputs found
Sort by
Selective 1,4-syn-Carboamination of Cyclic 1,3-Dienes via Hybrid Palladium Catalysis
No full text1,4-cis-disubstituted cyclic compounds play a pivotal role in pharmaceutical development, offering enhanced potency and bioavailability. However, their stereoselective and modular synthesis remains a long-standing challenge. Here, we report an innovative strategy for accessing these structures via mild conditions employing cyclic 1,3-dienes/alkyl(aryl)halides and amines. This procedure exhibits a wide substrate scope that tolerates various functional groups. The utility of this method is demonstrated in the efficient synthesis of a TRPV6 inhibitor, CFTR modulator and other bioactive molecules. Combined experimental and computational studies suggest that the hybrid palladium-catalyzed radical-polar crossover mechanism is crucial for achieving the exceptional 1,4-syn-addition selectivity (dr > 20:1)
Electromigrated gold nanogap tunnel junction arrays: Fabrication and electrical behavior in liquid and gaseous media
No full textTunnel junctions have been suggested as high-throughput electronic single molecule sensors in liquids, with several seminal experiments conducted using break junctions with reconfigurable gaps. For practical single molecule sensing applications, arrays of on-chip integrated fixed-gap tunnel junctions that can be built into compact systems are preferable. Fabricating nanogaps by electromigration is one of the most promising approaches to realize on-chip integrated tunnel junction sensors. However, the electrical behavior of fixed-gap tunnel junctions immersed in liquid media has not been systematically studied to date, and the formation of electromigrated nanogap tunnel junctions in liquid media has not yet been demonstrated. In this work, we perform a comparative study of the formation and electrical behavior of arrays of gold nanogap tunnel junctions made by feedback-controlled electromigration immersed in various liquid and gaseous media (deionized water, mesitylene, ethanol, nitrogen, and air). We demonstrate that tunnel junctions can be obtained from microfabricated gold nano-constrictions inside liquid media. Electromigration of junctions in air produces the highest yield (61 %), electromigration in deionized water and mesitylene results in a lower yield than in air (44–48 %), whereas electromigration in ethanol fails to produce viable tunnel junctions due to interfering electrochemical processes. We map out the stability of the conductance characteristics of the resulting tunnel junctions and identify medium-specific operational conditions which have an impact on the yield of forming stable junctions. Furthermore, we highlight the unique challenges associated with working with arrays of large numbers of tunnel junctions in batches. Our findings will inform future efforts to build single-molecule sensors using on-chip integrated tunnel junctions
Direct C-H Electrophilic Borylation with (C6F5)2B-NTf2 to Generate B-N Dibenzo[a,h]pyrenes
No full textThe borylation of aryl substituted pyridines is an effective way of preparing B-N doped conjugated organic frameworks. Trihaloborane Lewis acids are often employed for this protocol, and may require further functionalization to replace the remaining halides on boron. We report a new, fully characterized, electrophilic borylating agent, (C6F5)2B(2-NTf2), that smoothly incorporates a -B(C6F5)2 unit into the model substrate 2-phenylpyridine. To demonstrate its utility in preparing more complex B-N doped structures, we use it to prepare seven examples of the 6a,13a-diaza-7,14-dibora-dibenzo[a,h]pyrene framework, with substituents of varying donor properties. The structural, redox and photophysical properties of this new family of B-N doped polycyclic hydrocarbon compounds were probed experimentally and computationally
Synthesis of proposed structures and structural revision of marine Roseobacter sulfur amino lipids (SALs)
No full textThe Roseobacter Clade Bacteria (RCB) play a crucial role in marine ecosystems, particularly Ruegeria pomeroyi and Phaeobacter inhibens, which utilize organosulfur compounds such as 2,3-dihydroxypropanesulfonate (DHPS) and dimethylsulfoniopropionoate (DMSP). Recently, a new class of sulfonolipids, sulfur amino lipids (SALs), was identified in these bacteria, with possible structures proposed by Smith et al. (ISME Journal, 2021, 15, 2440-2453). This study aims to confirm or revise the proposed structures for SAL-656 and SAL-672. Two candidates for SAL-656 and SAL-672 were synthesized, namely 3-acyloxyacylamides of homotaurine and cysteinolic acid, respectively. Tandem mass spectrometry (MS/MS) analysis of synthetic and natural SALs revealed significant discrepancies, leading to the exclusion of proposed structures. Further exploration of lipid extracts from R. pomeroyi and P. inhibens identified related lipoforms of SAL-656 and SAL-672, which form two distinct families based on LC-MS/MS and molecular network analysis. While the mass spectrometric data allow exclusion of previously proposed structures and provide insights into acyl groups and headgroups, the complete structures of SAL-656 and SAL-672 remain elusive. Nonetheless, the data are consistent with revised structures based on cysteinolic acid or 3-amino-2-hydroxypropanesulfonic acid wherein both hydroxyl and amino groups are acylated. Our findings reveal the SALs as a group of sulfonolipids that are distinct from more studied classes of sulfonolipids, with implications for understanding their biosynthesis and ecological roles in marine environments
Speciation of the proton in water-in-salt electrolytes
No full textWater-in-salt (WiS) electrolytes are promising systems for a variety of energy storage devices. Indeed, they represent a great alternative to conventional organic electrolytes thanks to their environmental friendliness, non-flammability, and good electrochemical stability. Understanding the behaviour of such systems and their local organisation is a key direction for their rational design and successful implementation at the industrial scale. In the present paper, we focus our investigation on the 21 m bis(trifluoromethanesulfonyl)imide (LiTFSI) WiS electrolyte, recently reported to have acidic pH values. We explore the speciation of an excess proton in this system and its dependence on the initial local environment using ab initio molecular dynamics simulations. In particular, we observe the formation of HTFSI acid in WiS system, known to act as a superacid in water. This acid is stabilised in the WiS solution for several picoseconds thanks to the formation of a complex with water molecules and a neighboring TFSI– anion. We further investigate how the excess proton affects the microstructure of WiS, in particular, the oligomerisation of lithium cations, and report possible notable perturbations of lithium nanochain organisation in some cases. These two phenomena are particularly important when considering WiS as electrolytes in batteries and supercapacitors, and our results contribute to the comprehension of these systems on the molecular level
Control of energy transfer by tuning Donor/Acceptor interface for exciplex upconversion-type organic light-emitting diodes
No full textOrganic light-emitting diodes (OLEDs) generally require operating voltage corresponding to the energy gap of the emitter. To reduce operating voltage, exciplex upconversion-type OLEDs (ExUC-OLEDs) have been reported. ExUC-OLEDs emit light via triplet-triplet upconversion (TTU) by transferring the energy of the exciplex to T1 of the emitter. Therefore, a combination of donor and acceptor that form an appropriate exciplex for energy transfer is required, and the degree of freedom in material selection for ExUC-OLEDs is low. Herein, the insertion of the spacer at the donor/acceptor interface controls the coulombic attraction, the energy of the exciplex, and the energy transfer to the emitter. As a result, ExUC emission is maintained up to 3 nm spacer thickness, and the external quantum efficiency is improved. Therefore, it is possible to increase the degree of freedom in material selection for ExUC-OLEDs by using appropriate spacer materials
Aquamarine: Quantum-Mechanical Exploration of Conformers and Solvent Effects in Large Drug-like Molecules
No full textWe here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM) dataset that contains the structural and electronic information of 59,783 low-and high-energy conformers of 1,653 molecules with a total number of atoms ranging from 2 to 92 (mean:50.9), and containing up to 54 (mean:28.2) non-hydrogen atoms. To gain insights into the solvent effects as well as collective dispersion interactions for drug-like molecules, we have performed QM calculations supplemented with a treatment of many-body dispersion (MBD) interactions of structures and properties in the gas phase and implicit water. Thus, AQM contains over 40 global (molecular) and local (atom-in-a-molecule) physicochemical properties (including ground-state and response properties) per conformer computed at the tightly converged PBE0+MBD level of theory for gas-phase molecules, whereas PBE0+MBD with the modified Poisson-Boltzmann (MPB) model of water was used for solvated molecules. By addressing both molecule-solvent and dispersion interactions, AQM dataset can serve as a challenging benchmark for state-of-the-art machine learning methods for property modeling and \textit{de novo} generation of large (solvated) molecules with pharmaceutical and biological relevance
Toward High Selectivity of Sensor Arrays: Enhanced Adsorption Interaction and Selectivity of Gas Detection (N2, O2, NO, CO, CO2, NO2, SO2, AlH3, NH3, and PH3) on Transition Metal Dichalcogenides (MoS2, MoSe2, and MoTe2)
No full textResistive gas sensors are essential for monitoring air quality, ensuring industrial safety, and controlling automotive emissions. However, conventional materials used for sensing layers often suffer from poor selectivity and require elevated operating temperatures, limiting their effectiveness. This study introduces a novel approach to address these challenges by utilizing the intrinsic physicochemical properties of Mo-bearing transition-metal dichalcogenides (TMDs). The findings reveal that variable charge availability on TMD surfaces leads to highly selective adsorption enhancements, resulting in significant differences in the TMD responses to various molecules, even at room temperature. This results in an exceptional relative sensitivity of the TMD monolayers, which in the case of combustion products exceeds what is feasible under the same conditions by conventional sensing materials such as ZnO and TiO2 by three orders of magnitude. Such an unprecedented variation in responses results in distinct sensing profiles. This enables effective cross-referencing of responses, offering significant benefits for sensor arrays. Consequently, even in relatively simple setups, TMD-based devices have the potential to prevent false-positive signals and even enable the determination of the composition of gas mixtures, which, if utilized, could revolutionize the field of gas monitoring with innovative lab-on-a-chip solutions
Molecular Fingerprints Optimization for Enhanced Predictive Modeling
No full textThe human exposome is represented by a vast number of chemicals, the fate and behavior of which remain largely unexplored. While modeling approaches are commonly employed to address this challenge, there is a recognized need for alternative molecular representations, such as molecular fingerprints. However, existing algorithms for computing molecular fingerprints may incorporate irrelevant or insufficient information for accurate activity prediction. In this study, we present an algorithm designed to optimize molecular fingerprints. This algorithm combines the relevant bits of information, aiming to enrich the final fingerprint for predicting specific behavioral properties. To achieve this, relevant variables (i.e. bits) for prediction were collected from six non-hashed fingerprints and fused into a master fingerprint. We used fish toxicity as a proof of concept. The RFR model was developed based on the master fingerprint. It demonstrated comparable results to conventional descriptor-based models with R for the training set and R for the test set. The molecular fingerprints have the advantage of being consistent and interpretable. Consequently, we were able to confirm the relevance of variables to the toxicity prediction. The final model outperformed each of the models based on individual fingerprints in the number of chemicals with prediction error, that fell in the range of +/- one standard deviation of residuals. The number of cases with the lower prediction error was on average four times higher for the master fingerprint-based model. The algorithm developed for optimizing molecular fingerprints is universal and can be applied to various case studies
What is the appropriate data representation of electrochemical impedance spectroscopy in machine-learning analysis?
No full textElectrochemical impedance spectroscopy (EIS) is an important analytic technique for the understanding of electrochemical systems. With the recent advent and burgeoning deployment of machine learning (ML) in EIS analysis, a critical yet hitherto unanswered question emerges: what is the appropriate data representation of EIS for ML-based analysis? While the representation of a model’s input data is known to be critical for a successful deployment of ML model, EIS is known to possess multiple classical venues of data representation and it remains unclear how different EIS data should be compared following a proper data normalization protocol. Here we report the methodology and the outcomes that evaluate the efficacy of multiple data representation methods in ML-based EIS analysis. At least within our proof-of-concept parameter space, plotting the input training data’s impedance magnitude (|Z|) against phase angle (φ) while individually normalizing each EIS curve yields the highest accuracy and robustness in the correspondingly established residual neural network (ResNet) model. Rationalized by additional "importance" analysis of the input data, such a data representation method extracts information and hidden features more effectively. While Nyquist plot is more widely used in manual analysis, we found that ML-based analysis may require a different data representation and offered a clear guideline for future researchers to evaluate on a case-by-case basis