Exploring structure-property landscape of non-fullerene acceptors for organic solar cells.

We present a comprehensive analysis of the structure-property relationship in small molecule non-fullerene acceptors (NFAs) featuring an acceptor-donor-acceptor configuration employing state-of-the-art quantum chemical computational methods. Our focus lies in the strategic functionalization of halogen groups at the terminal positions of NFAs as an effective means to mitigate non-radiative voltage losses and augment photovoltaic and photophysical properties relevant to organic solar cells. Through photophysical studies, we observe a bathochromic shift in the visible region for all halogen-functionalized NFAs, except type-2, compared to the unmodified compound. Most of these functionalized compounds exhibit exciton binding energies below 0.3 eV and ΔLUMO less than 0.3 eV, indicating their potential as promising candidates for organic solar cells. Selected candidate structures undergo an analysis of charge transport properties using the semi-classical Marcus theory based on hopping transport formalism. Molecular dynamics simulations followed by charge transport simulations reveal an ambipolar nature of charge transport in the investigated NFAs, with equivalent hole and electron mobilities compared to the parent compound. Our findings underscore the crucial role of end-group functionalization in enhancing the photovoltaic and photophysical characteristics of NFAs, ultimately improving the overall performance of organic solar cells. This study advances our understanding of the structure-property relationships in NFAs and provides valuable insights into the design and optimization of organic solar cell materials.

Improved Assessment of Schistosoma Community Infection Through Data Resampling Method.

Background: The conventional diagnostic for Schistosoma mansoni infection is stool microscopy with the Kato-Katz (KK) technique to detect eggs. Its outcomes are highly variable on a day-to-day basis and may lead to biased estimates of community infection used to inform public health programs. Our goal is to develop a resampling method that leverages data from a large-scale randomized trial to accurately predict community infection. Methods: We developed a resampling method that provides unbiased community estimates of prevalence, intensity and other statistics for S mansoni infection when a community survey is conducted using KK stool microscopy with a single sample per host. It leverages a large-scale data set, collected in the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) project, and allows linking single-stool specimen community screening to its putative multiday "true statistics." Results: SCORE data analysis reveals the limited sensitivity of KK stool microscopy and systematic bias of single-day community testing versus multiday testing; for prevalence estimate, it can fall up to 50% below the true value. The proposed SCORE cluster method reduces systematic bias and brings the estimated prevalence values within 5%-10% of the true value. This holds for a broad swath of transmission settings, including SCORE communities, and other data sets. Conclusions: Our SCORE cluster method can markedly improve the S mansoni prevalence estimate in settings using stool microscopy.

Cost-Effective Approach for Modeling of Multiresonant Thermally Activated Delayed Fluorescence Emitters.

Multiresonant thermally activated delayed fluorescence (MR-TADF) emitters have recently attracted great interest for application in organic light-emitting diodes due to their remarkable electroluminescent efficiency and narrow emission spectra. It is therefore essential to establish computational methodologies that can accurately model the excited states of these materials at manageable computational costs. With regard to MR-TADF design and their associated photophysics, previous works have highlighted the importance of wave function-based methods, at much higher computational costs, over the traditional time-dependent density functional theory approach. Herein, we employ two independent techniques built on different quantum mechanical frameworks, highly correlated wave function-based STEOM-DLPNO-CCSD and range-separated double hybrid density functional, TD-B2PLYP, to investigate their performance in predicting the excited state energies in MR-TADF emitters. We demonstrate a remarkable mean absolute deviation (MAD) of ∼0.06 eV in predicting ΔEST compared to experimental measurements across a large pool of chemically diverse MR-TADF molecules. Furthermore, both methods yield superior MAD in estimating S1 and T1 energies over earlier reported SCS-CC2 computed values [J. Chem. Theory Comput. 2022, 18, 4903]. The short-range charge-transfer nature of low-lying excited states and narrow fwhm values, hallmarks of this class of emitters, are precisely captured by both approaches. Finally, we show the transferability and robustness of these methods in estimating rates of radiative and nonradiative events with adequate agreement against experimental measurements. Implementing these cost-effective computational approaches is poised to streamline the identification and evaluation of potential MR-TADF emitters, significantly reducing the reliance on costly laboratory synthesis and characterization processes.

Constructing Multiresonance Thermally Activated Delayed Fluorescence Emitters for Organic LEDs: A Computational Investigation.

Multiple resonance thermally activated delayed fluorescence (MR-TADF) materials have acquired substantial attention due to their high electroluminescence efficiency with narrow emission spectra. However, the existing MR-TADF emitters suffer from substantial efficiency roll-off due to insufficient rate constants of the reverse intersystem crossing (kRISC) process compared to the traditional TADF materials. Herein, we employ the DLPNO-STEOM-CCSD method, which is computationally less expensive than the wave function-based EOM-CCSD method, to evaluate the electronic and photophysical properties of MR-TADF materials accurately. The predicted singlet-triplet energy gap (ΔEST), one of the critical parameters governing the TADF efficiency, exhibits remarkable agreement with the experimental measurement, with a standard deviation value of 0.026 eV (obtained based on five experimentally synthesized MR-TADF systems). The proposed technique was utilized to determine the suitability of 15 newly designed MR-TADF emitters via their computed radiative and nonradiative rates, luminescence efficiencies, and exciton characteristics. Moreover, most conceived molecules exhibit blue emission with decent to strong oscillator strengths, making them potential candidates for practical light-emitting applications. The proposed computational route will undoubtedly accelerate the designing and prescreening of potential MR-TADF emitters before their expensive laboratory synthesis and characterization.

Polarity and orbital driven reduction in contact resistance in organic devices with functionalized electrodes.

Interlayers at electrode interfaces have been shown to reduce contact resistance in organic devices. However, there still needs to be more clarity regarding the role of microscopic properties of interlayer functionalized interfaces on device behavior. Here, we show that the impact of functionalized electrodes on device characteristics can be predicted by a few critical computationally derived parameters representing the interface charge distribution and orbital interactions. The significant influences of interfacial orbital interactions and charge distribution over device and interface properties are exhibited. Accordingly, a function is developed based on these parameters that capture their effect on the interface resistance. A strong correlation is observed, such that enhanced orbital interactions and reduced charge separation at the interface correspond to low resistance regardless of the individual molecules utilized as the interlayer. The charge distribution and orbital interactions vary with the molecular structure of the interlayer, allowing the tuning of device characteristics. Hence, the proposed function serves as a guideline for molecular design and selection for interlayers in organic devices.

Ligand-mediated electron transport channels enhance photocatalytic activity of plasmonic nanoparticles.

Photoexcitation of noble metal nanoparticles creates surface plasmons which further decay to form energetic charge carriers. These charge carriers can initiate and/or accelerate various chemical processes at nanoparticle surfaces, although the efficiency of such processes remains low as a large fraction of these carriers recombine before they can reach the reaction sites. Thus efficient utilization of these charge carriers requires designing nanostructures that promote the separation of charges and their transport toward the reaction sites. Here we demonstrate that covalently bound surface-coating ligands with suitable orbital alignment can provide electron transport channels boosting hot electron extraction from a gold nanostructure leading to a huge enhancement in the rate of hydrogen evolution reaction (HER) under NIR excitation. A (p)Br-Ph-SH substituted gold nanoprism (AuTP) substrate produced ∼4500 fold more hydrogen compared to a pristine AuTP substrate under 808 nm excitation. Further experimental and theoretical studies on a series of substituted benzene-thiol bound AuTP substrates showed that the extent of the ligand-mediated HER enhancement depends not only on the polarity of the ligand but on the interfacial orbitals interactions.

Understanding the Microscopic Origin of the Contact Resistance at the Polymer-Electrode Interface.

Contact resistance (RC) in organic devices originates from a mismatch in energy levels between injecting electrodes and organic semiconductors (OSCs). However, the microscopic effects governing charge transfer between electrodes and the OSCs have not been analyzed in detail. We fabricated transistors with different OSCs (PTB7, PCDTBT, and PTB7-Th) and electrodes (MoO3, Au, and Ag) and measured their contact resistance. Regardless of the electrodes, devices with PTB7-Th exhibit the lowest values of RC. To explain the trends observed, first-principles computations were performed on contact interfaces based on the projector operator diabatization method. Our results revealed that differences in energy levels and the electronic couplings between OSCs' highest occupied molecular orbitals and vacant states on the electrodes influence device RC. Further, based on values obtained from the first-principles, the rate of charge transfer between OSCs and electrodes is calculated and found to correlate strongly with trends in RC for devices with different OSCs. We thus show that device RC is governed by the feasibility of charge transfer at the contact interface and hence determined by energy levels and electronic coupling among orbitals and states located on OSCs and electrodes.

Robust Zinc(II)porphyrin Catalyst for Visible Light Induced C-H Arylation of Heteroarenes.

Zinc(II)porphyrin catalyzed light induced C-H arylation of heteroarenes from anilines is discussed. The method is nontoxic and efficient, using only 0.5 mol % of porphyrin catalyst to produce bi(hetero)aryls in good yields. This work demonstrates the potential use of porphyrin photocatalysts as efficient and robust alternatives to organic dyes.

Small Molecule AIEgens for Illuminating Sub-Cellular Endoplasmic Reticulum, Mitochondria, and Lysosomes.

Organelles are the working hubs of the cells. Hence, visualizing these organelles inside the cells is highly important for understanding their roles in pathological states and development of therapeutic strategies. Herein, we report the development of a novel highly substituted oxazoles with modular scaffolds (AIE-ER, AIE-Mito, and AIE-Lyso), which can home into endoplasmic reticulum (ER), mitochondria, and lysosomes inside the cells. These oxazoles showed remarkable aggregation-induced emission (AIE) property in water and in the solid state due to dual intramolecular H-bonding, which was confirmed by pH- and temperature-dependent fluorescence studies followed by molecular dynamics (MD) simulations and density functional theory (DFT) calculations. Confocal laser scanning microscopy studies revealed that AIE-ER, AIE-Mito, and AIE-Lyso efficiently homed into ER, mitochondria and lysosomes, respectively, in the HeLa cervical cancer cells and non-cancerous human retinal pigment epithelial RPE-1 cells within 3 h without showing any toxicity to the cells with high sub-cellular photostability. To the best of our knowledge, this is the first report of highly substituted oxazole-based small molecule AIEgens for organelle imaging. We anticipate these novel AIEgens have promise to image sub-cellular organelles in different diseased states as well as understanding the inter-organelle interactions towards the development of novel therapeutics.

Rational design of non-fullerene acceptors via side-chain and terminal group engineering: a computational study.

We investigated the optoelectronic and photovoltaic properties of three types of acceptor-donor-acceptor-based non-fullerene acceptor (NFA) molecules for organic solar cell (OSC) applications. Density functional theory and its time-dependent variant were employed to compute the quadrupole moment perpendicular to the π-system (Q20), open circuit voltage (VOC), and other relevant solar cell parameters. The role of functionalization in the acceptor unit on the overall device performance was explored by incorporating halogen and methoxy-based electron-withdrawing groups. The electronegativity differences between the halogen atoms and the methoxy group demonstrated contrasting effects on the energy levels, molecular orbitals, and absorption maximum. We observed a trade-off between short-circuit current (JSC) and VOC, which was further substantiated by an inverse correlation between Q20 and VOC. We found an optimum value of Q20 in the range of 80 to 130 ea02 to achieve an optimized solar cell performance. Among the designed systems, Se-derived NFAs with a small band gap, red-shifted absorption maximum, high-oscillator strength, small exciton binding energy, and optimum Q20 turned out to be potential candidates for future applications. These criteria can be generalized to design and screen next-generation non-fullerene acceptors to achieve improved OSC performance.

Cellulose Nanocrystal-Based All-3D-Printed Pyro-Piezoelectric Nanogenerator for Hybrid Energy Harvesting and Self-Powered Cardiorespiratory Monitoring toward the Human-Machine Interface.

Biomaterials with spontaneous piezoelectric property are highly emerging in recent times for the generation of electricity from mechanical energy sources that are amply available in nature. In this context, pyroelectricity, an integral property of piezoelectric materials, might be an interesting tool in harvesting thermal energy from the fluctuations of temperature. On the other hand, respiration and heart pulse are the significant human vital signs that can be used for early detection and prevention of cardiorespiratory diseases. Here, we report an all-three-dimensional (3D)-printed pyro-piezoelectric nanogenerator (Py-PNG) based on the most abundant and completely biodegradable biopolymer on earth, i.e., cellulose nanocrystal (CNC) for hybrid (mechanical as well as thermal) energy harvesting, and interestingly, the NG could be used as an e-skin sensor for application in self-powered noninvasive cardiorespiratory monitoring for personal healthcare. Notably, the CNC-based device will be biocompatible and economically advantageous due to its biomaterial-based supremacy and huge availability. This is an original approach with 3D geometrical advancement in designing a NG/sensor, where the unique all-3D-printed manner is adopted, and certainly, it has promising potential in reducing the number of processing steps to required equipment during the multilayer fabrication. The all-3D-printed NG/sensor shows outstanding mechano-thermal energy harvesting performance along with sensitivity and is capable of accurate detection of heart pulse as well as respiration, whenever and whichever required without the need of any battery or an external power supply. In addition, we have also extended its application in demonstrating a smart mask-based breath monitoring system. Thus, the real-time cardiorespiratory monitoring provides notable and fascinating information in medical diagnosis, stepping toward biomedical device development and human-machine interface.

Modeling Chemical Reactions in Alkali Carbonate-Hydroxide Electrolytes with Deep Learning Potentials.

We developed a deep potential machine learning model for simulations of chemical reactions in molten alkali carbonate-hydroxide electrolyte containing dissolved CO2, using an active learning procedure. We tested the deep neural network (DNN) potential and training procedure against reaction kinetics, chemical composition, and diffusion coefficients obtained from density functional theory (DFT) molecular dynamics calculations. The DNN potential was found to match DFT results for the structural, transport, and short-time chemical reactions in the melt. Using the DNN potential, we extended the time scales of observation to 2 ns in systems containing thousands of atoms, while preserving quantum chemical accuracy. This allowed us to reach chemical equilibrium with respect to several chemical species in the melt. The approach can be generalized for a broad spectrum of chemically reactive systems.

Chemically Driven Rotatory Molecular Machines.

Molecular machines are at the frontier of biology and chemistry. The ability to control molecular motion and emulating the movement of biological systems are major steps towards the development of responsive and adaptive materials. Amazing progress has been seen for the design of molecular machines including light-induced unidirectional rotation of overcrowded alkenes. However, the feasibility of inducing unidirectional rotation about a single bond as a result of chemical conversion has been a challenging task. In this Review, an overview of approaches towards the design, synthesis, and dynamic properties of different classes of atropisomers which can undergo controlled switching or rotation under the influence of a chemical stimulus is presented. They are categorized as molecular switches, rotors, motors, and autonomous motors according to their type of response. Furthermore, we provide a future perspective and challenges focusing on building sophisticated molecular machines.

γ-Resorcyclic Acid-Based AIEgens for Illuminating Endoplasmic Reticulum.

Endoplasmic reticulum (ER) has emerged as one of the interesting sub-cellular organelles due to its role in myriads of biological phenomena. Subsequently, visualization of the structure-function and dynamics of ER remained a major challenge to understand its involvement in different diseased states including cancer. To illuminate the ER, herein we have designed and synthesized γ-resorcyclic acid-based small molecules, which showed remarkable aggregation-induced emission (AIE) property in water. This AIE property was originated from the dual intramolecular H-bonding leading to the self-assembled 2D aggregation confirmed by pH- and temperature-dependent fluorescence quenching studies as well as scanning electron microscopy. These small molecules illuminated the sub-cellular ER in HeLa cervical cancer cells as well as non-cancerous RPE-1 human retinal epithelial cells within 1 h. These novel small molecules have the potential to light up ER chemical biology in diseased states.

Computational Design, Synthesis, and Photochemistry of Cy7-PPG, an Efficient NIR-Activated Photolabile Protecting Group for Therapeutic Applications.

Photolabile Protecting Groups (PPGs) are molecular tools used, for example, in photopharmacology for the activation of drugs with light, enabling spatiotemporal control over their potency. Yet, red-shifting of PPG activation wavelengths into the NIR range, which penetrates the deepest in tissue, has often yielded inefficient or insoluble molecules, hindering the use of PPGs in the clinic. To solve this problem, we report herein a novel concept in PPG design, by transforming clinically-applied NIR-dyes with suitable molecular orbital configurations into new NIR-PPGs using computational approaches. Using this method, we demonstrate how Cy7, a class of NIR dyes possessing ideal properties (NIR-absorption, high molecular absorptivity, excellent aqueous solubility) can be successfully converted into Cy7-PPG. We report a facile synthesis towards Cy7-PPG from accessible precursors and confirm its excellent properties as the most redshifted oxygen-independent NIR-PPG to date (λmax =746 nm).

Mining subgraph coverage patterns from graph transactions.

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.

Individual-based modeling of COVID-19 transmission in college communities.

The ongoing COVID-19 pandemic has created major public health and socio-economic challenges across the United States. Among them are challenges to the educational system where college administrators are struggling with the questions of how to mitigate the risk and spread of diseases on their college campus. To help address this challenge, we developed a flexible computational framework to model the spread and control of COVID-19 on a residential college campus. The modeling framework accounts for heterogeneity in social interactions, activities, environmental and behavioral risk factors, disease progression, and control interventions. The contribution of mitigation strategies to disease transmission was explored without and with interventions such as vaccination, quarantine of symptomatic cases, and testing. We show that even with high vaccination coverage (90%) college campuses may still experience sizable outbreaks. The size of the outbreaks varies with the underlying environmental and socio-behavioral risk factors. Complementing vaccination with quarantine and mass testing was shown to be paramount for preventing or mitigating outbreaks. Though our quantitative results are likely provisional on our model assumptions, sensitivity analysis confirms the robustness of their qualitative nature.

Pd-catalyzed sp-sp3 cross-coupling of benzyl bromides using lithium acetylides.

Organolithium-based cross-coupling reactions have emerged as an indispensable method to construct C-C bonds. These transformations have proven particularly useful for the direct and fast coupling of various organolithium reagents (sp, sp2, and sp3) with aromatic (pseudo) halides (sp2). Here we present an efficient method for the cross-coupling of benzyl bromides (sp3) with lithium acetylides (sp). The reaction proceeds within 10 min at room temperature and can be performed in the presence of organolithium-sensitive functional groups such as esters, nitriles, amides and boronic esters. The potential application of the methodology is demonstrated in the preparation of key intermediates used in pharmaceuticals, chemical biology and natural products.

SARS-CoV-2 transmission and control in a hospital setting: an individual-based modelling study.

Development of strategies for mitigating the severity of COVID-19 is now a top public health priority. We sought to assess strategies for mitigating the COVID-19 outbreak in a hospital setting via the use of non-pharmaceutical interventions. We developed an individual-based model for COVID-19 transmission in a hospital setting. We calibrated the model using data of a COVID-19 outbreak in a hospital unit in Wuhan. The calibrated model was used to simulate different intervention scenarios and estimate the impact of different interventions on outbreak size and workday loss. The use of high-efficacy facial masks was shown to be able to reduce infection cases and workday loss by 80% (90% credible interval (CrI): 73.1-85.7%) and 87% (CrI: 80.0-92.5%), respectively. The use of social distancing alone, through reduced contacts between healthcare workers, had a marginal impact on the outbreak. Our results also indicated that a quarantine policy should be coupled with other interventions to achieve its effect. The effectiveness of all these interventions was shown to increase with their early implementation. Our analysis shows that a COVID-19 outbreak in a hospital's non-COVID-19 unit can be controlled or mitigated by the use of existing non-pharmaceutical measures.

Photo-responsive Helical Motion by Light-Driven Molecular Motors in a Liquid-Crystal Network.

Controlling sophisticated motion by molecular motors is a major goal on the road to future actuators and soft robotics. Taking inspiration from biological motility and mechanical functions common to artificial machines, responsive small molecules have been used to achieve macroscopic effects, however, translating molecular movement along length scales to precisely defined linear, twisting and rotary motions remain particularly challenging. Here, we present the design, synthesis and functioning of liquid-crystal network (LCN) materials with intrinsic rotary motors that allow the conversion of light energy into reversible helical motion. In this responsive system the photochemical-driven molecular motor has a dual function operating both as chiral dopant and unidirectional rotor amplifying molecular motion into a controlled and reversible left- or right-handed macroscopic twisting movement. By exploiting the dynamic chirality, directionality of motion and shape change of a single motor embedded in an LC-network, complex mechanical motions including bending, walking and helical motion, in soft polymer materials are achieved which offers fascinating opportunities toward inherently photo-responsive materials.