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DOE OSTI2023

Development of LpxH Inhibitors Chelating the Active Site Dimanganese Metal Cluster of LpxH

Abstract Despite the widespread emergence of multidrug‐resistant nosocomial Gram‐negative bacterial infections and the major public health threat it brings, no new class of antibiotics for Gram‐negative pathogens has been approved over the past five decades. Therefore, there is an urgent medical need for developing effective novel antibiotics against multidrug‐resistant Gram‐negative pathogens by targeting previously unexploited pathways in these bacteria. To fulfill this crucial need, we have been investigating a series of sulfonyl piperazine compounds targeting LpxH, a dimanganese‐containing UDP‐2,3‐diacylglucosamine hydrolase in the lipid A biosynthetic pathway, as novel antibiotics against clinically important Gram‐negative pathogens. Inspired by a detailed structural analysis of our previous LpxH inhibitors in complex with K. pneumoniae LpxH ( Kp LpxH), here we report the development and structural validation of the first‐in‐class sulfonyl piperazine LpxH inhibitors, JH‐LPH‐45 ( 8 ) and JH‐LPH‐50 ( 13 ), that achieve chelation of the active site dimanganese cluster of Kp LpxH. The chelation of the dimanganese cluster significantly improves the potency of JH‐LPH‐45 ( 8 ) and JH‐LPH‐50 ( 13 ). We expect that further optimization of these proof‐of‐concept dimanganese‐chelating LpxH inhibitors will ultimately lead to the development of more potent LpxH inhibitors for targeting multidrug‐resistant Gram‐negative pathogens.

Pharmacology & Pharmacy↗

DOE OSTI2025

Toward a Generalizable Prediction Model of Molten Salt Mixture Density with Chemistry‐Informed Transfer Learning

Optimally designing applications of molten salts requires knowledge of their thermophysical properties over a wide range of temperatures and compositions. There exist significant gaps in existing databases and this data can be challenging to experimentally measure due to high temperatures, salt corrosivity, and salt hygroscopicity. Existing databases have been used to create Redlich–Kister (RK) models for mixture density showing improved accuracy with respect to ideal mixing assumptions, but these models require subcomponent data measurements for each new system, therefore lacking generality. In order to address generalizability and data sparsity, a transfer learning procedure is proposed to train deep neural networks (DNNs) using a combination of semi‐empirical relationships (RK), data from the thermophysical arm of the molten salt thermal properties database and universal ab initio properties of component mixtures taken from the joint automated repository for various integrated simulations (JARVIS) classical force‐field inspired descriptors database to predict density in molten salts. Herein, it is shown that DNNs predict molten salt density with an r 2 over 0.99 and a mean absolute percentage error under 1%, outperforming alternative methods.

inorganic materials↗

DOE OSTI2023

Light‐driven Transformation of Carbon Monoxide into Hydrocarbons using CdS@ZnS : VFe Protein Biohybrids

Enzymatic Fisher-Tropsch (FT) process catalyzed by vanadium (V)-nitrogenase can convert carbon monoxide (CO) to longer-chain hydrocarbons (>C2) under ambient conditions, although this process requires high-cost reducing agent(s) and/or the ATP-dependent reductase as electron and energy sources. Using visible light-activated CdS@ZnS (CZS) core-shell quantum dots (QDs) as alternative reducing equivalent for the catalytic component (VFe protein) of V-nitrogenase, we first report a CZS : VFe biohybrid system that enables effective photo-enzymatic C−C coupling reactions, hydrogenating CO into hydrocarbon fuels (up to C4) that can be hardly achieved with conventional inorganic photocatalysts. Surface ligand engineering optimizes molecular and opto-electronic coupling between QDs and the VFe protein, realizing high efficiency (internal quantum yield >56 %), ATP-independent, photon-to-fuel production, achieving an electron turnover number of >900, that is 72 % compared to the natural ATP-coupled transformation of CO into hydrocarbons by V-nitrogenase. The selectivity of products can be controlled by irradiation conditions, with higher photon flux favoring (longer-chain) hydrocarbon generation. The CZS : VFe biohybrids not only can find applications in industrial CO removal for high-value-added chemical production by using the cheap, renewable solar energy, but also will inspire related research interests in understanding the molecular and electronic processes in photo-biocatalytic systems.

Chemistry↗

DOE OSTI2024

Evaluation of Aliphatic Alcohols for CO 2 Capture Using the Characteristic Carbonate Frequency

Control over CO 2 capture and utilization are important scientific and technological challenges. Although a variety of amine absorbents are used for capture, releasing the captured CO 2 is often difficult and limits their recyclability. Therefore, it is crucial to control the strength of the CO 2 bond with the absorbent. Furthermore, it is desirable to use a method that can conveniently report the strength of this bond. This motivates exploring adducts of CO 2 with alcohols in the presence of a base, using vibrational spectroscopy to report on the bond strength. Although reactions of alcohols with CO 2 to form alkyl carbonates are known, a systematic study of these adducts has not been conducted. Here we show formation of alkyl carbonates by a series of alcohols spanning the pK a range 9.5 to 16.8. We show experimental and computational results for the frequency of the characteristic asymmetric stretch of the carbonate and demonstrate that it correlates inversely with the pK a of the alcohol. Based on computations of the bond lengths and previous work, we propose that this frequency also correlates inversely with the adduct strength. As a result, this work extends the scope of CO 2 capture reagents and inspires further research in tuning alcohols as reversible absorbents.

Alcohols↗

DOE OSTI2023

Predicting ecosystem metaphenome from community metagenome: A grand challenge for environmental biology

Abstract Elucidating how an organism's characteristics emerge from its DNA sequence has been one of the great triumphs of biology. This triumph has cumulated in sophisticated computational models that successfully predict how an organism's detailed phenotype emerges from its specific genotype. Inspired by that effort's vision and empowered by its methodologies, a grand challenge is described here that aims to predict the biotic characteristics of an ecosystem, its metaphenome, from nucleic acid sequences of all the species in its community, its metagenome. Meeting this challenge would integrate rapidly advancing abilities of environmental nucleic acids (eDNA and eRNA) to identify organisms, their ecological interactions, and their evolutionary relationships with advances in mechanistic models of complex ecosystems. Addressing the challenge would help integrate ecology and evolutionary biology into a more unified and successfully predictive science that can better help describe and manage ecosystems and the services they provide to humanity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Manufacturing Water‐Based Low‐Tortuosity Electrodes for Fast‐Charge through Pattern Integrated Stamping

Achieving high energy density and fast charging of lithium‐ion batteries can accelerate the promotion of electric vehicles. However, the increased mass loading causes poor charge transfer, impedes the electrochemical reaction kinetics, and limits the battery charging rate. Herein, this work demonstrated a novel pattern integrated stamping process for creating channels in the electrode, which benefits ion transport and increases the rate performance of the electrode. Meanwhile, the pressure applied during the stamping process improved the contact between electrode and current collector and also enhanced the mechanical stability of the electrode. Compared to the conventional bar‐coated electrode with the same thickness of 155 μm (delivered a discharge capacity of 16 mAh g −1 at the rate of 3 C), the stamped low‐tortuosity LiFePO 4 electrode delivered 101 mAh g −1 capacity. Additionally, water was employed as a solvent in this study. Owing to its eco‐friendliness, high scalability, and minimal waste generation, this novel stamping technique inspire a new method for the industrial‐level efficient roll to roll fabrication of fast‐charge electrodes.

25 ENERGY STORAGE↗

DOE OSTI2022

Bioinspired mechanically interlocking holey graphene@SiO 2 anode

Mechanically interlocking structures that can enhance adhesion at the interface and regulate the stress distribution have been widely observed in biological systems. Inspired by the biological structures in the wings of beetles, we synthesized a holey graphene@SiO 2 anode with strong mechanical interlocking, characterized it electrochemically, and explained its performance by finite element analysis and density functional calculations. The mechanically interlocking structure enhances lithium-ion (Li + ) storage by transmitting the strain from SiO 2 to the holey graphene and by a mechano-electrochemical coupling effect. The interlocking fit hinders the abscission of SiO 2 and the distinctive structure reduces the stress and strain of SiO 2 during (de)lithiation. The positive mechano-electrochemical coupling effect preserves the amount of electrochemically active phase (Li x Si) during cycles and facilitates Li + diffusion. Therefore, the capacity shows only a slight attenuation after 8000 cycles (cycling stability), and the specific capacity is ~1200 mA h g –1 at 5 A/g (rate-performance). This study furnishes a novel way to design high-performance Li + /Na + /K + /Al 3+ anodes with large volume expansion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Crystalline Supramolecular Polymers: Dynamics, Chirality, and Function

Inspired by biological systems, supramolecular polymers have emerged over the past three decades as an important way to form functional structures with potential to tune the internal order and dynamics and to create materials with the potential for responsiveness, reconfigurability, and recyclability. We focus here on several recent examples of ordered supramolecular polymers with a significant extent of internal order that results in crystalline molecular packing, or with dynamics that allow them to undergo structural reconfiguration. These examples highlight the spectrum of molecular mobility that the noncovalently bound constituents can experience. Furthermore, these materials have found interesting functions in their interaction with chiral biological systems and in their ability to form materials with interesting optoelectronic and photocatalytic behavior.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Positive Co‐Degree Density of Hypergraphs

The minimum positive co-degree of a nonempty r-graph H, denoted $δ^+_{r-1}$ (H), is the maximum k such that if S is an (r-1)-set contained in a hyperedge of H, then is contained in at least distinct hyperedges of H. Given an r-graph F, we introduce the positive co-degree Turán number co + ex(n, F) as the maximum positive co-degree $δ^+_{r-1}$ (H) over all n-vertex r-graphs H that do not contain F as a subhypergraph. In this paper, we concentrate on the behavior of co + ex(n, F) for 3-graphs F. In particular, we determine asymptotics and bounds for several well-known concrete 3-graphs F (e.g. $K^-_4$ and the Fano plane). Here, we also show that, for r-graphs, the limit γ + (F)≔ lim$_{n→∞}$ $\frac{co^+ex(n, F)}{n}$ exists, and “jumps” from 0 to 1/r, that is, it never takes on values in the interval . Moreover, we characterize which r-graphs F have γ + (F) = 0. Our motivation comes primarily from the study of (ordinary) co-degree Turán numbers where a number of results have been proved that inspire our results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Ionic Liquid Facilitated Solvent‐Phase Polymerization of Ultrasmooth Coatings of Polycatecholamines

Mussel-inspired polycatecholamine coatings and films have proven to be versatile materials for surface modification. However, the progress of chemically modified coatings with tailored properties has been slow due to the lack of definitive evidence of their polymeric structure. Polycatecholamines form aggregates during polymerization, and their formation mechanism has been a subject of debate owing to their insolubility in traditional molecular solvents. Exploiting the capability of ionic liquids (ILs) to exhibit strong electrostatic interactions, herein, the first solution-phase polymerization approach is reported to achieve polymerization of four catecholamines – dopamine (DA), norepinephrine (NE), L-3,4-dihydroxyphenylalanine (L-DOPA), and adrenaline (AD). Kinetic control over the polymerization process in the solution phase enabled it to follow the process with 1 H NMR spectroscopy, revealing the formation of structural features of resulting complex heterogeneous and soluble polymers. The polymers are both covalent and associative in nature. Solution phase polymerization prevented the formation of large aggregates in the solution, thus facilitating the formation of coatings that are ultra-smooth and have root mean square roughness of the order of sub-nm to nm scale.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Computational models of direct and indirect X‐ray breast imaging detectors for in silico trials

Abstract Background To facilitate in silico studies that investigate digital mammography (DM) and breast tomosynthesis (DBT), models replicating the variety in imaging performance of the DM and DBT systems, observed across manufacturers are needed. Purpose The main purpose of this work is to develop generic physics models for direct and indirect detector technology used in commercially available systems, with the goal of making them available open source to manufacturers to further tweak and develop the exact in silico replicas of their systems. Methods We recently reported on an in silico version of the SIEMENS Mammomat Inspiration DM/DBT system using an open‐source GPU‐accelerated Monte Carlo x‐ray imaging simulation code (MC‐GPU). We build on the previous version of the MC‐GPU codes to mimic the imaging performances of two other Food and Drug Administration (FDA)‐approved DM/DBT systems, such as Hologic Selenia Dimensions (HSD) and the General Electric Senographe Pristina (GSP) systems. In this work, we developed a hybrid technique to model the optical spread and signal crosstalk observed in the GSP and HSD systems. MC simulations are used to track each x‐ray photon till its first interaction within the x‐ray detector. On the other hand, the signal spread in the x‐ray detectors is modeled using previously developed analytical equations. This approach allows us to preserve the modeling accuracy offered by MC methods in the patient body, while speeding up secondary carrier transport (either electron–hole pairs or optical photons) using analytical equations in the detector. The analytical optical spread model for the indirect detector includes the depth‐dependent spread and collection of optical photons and relies on a pre‐computed set of point response functions that describe the optical spread as a function of depth. To understand the capabilities of the computational x‐ray detector models, we compared image quality metrics like modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE), simulated with our models against measured data. Please note that the purpose of these comparisons with measured data would be to gauge if the model developed as part of this work could replicate commercially used direct and indirect technology in general and not to achieve perfect fits with measured data. Results We found that the simulated image quality metrics such as MTF, NNPS, and DQE were in reasonable agreement with experimental data. To demonstrate the imaging performance of the three DM/DBT systems, we integrated the detector models with the VICTRE pipeline and simulated DM images of a fatty breast model containing a spiculated mass and a calcium oxalate cluster. In general, we found that the images generated using the indirect model appeared more blurred with a different noise texture and contrast as compared to the systems with direct detectors. Conclusions We have presented computational models of three commercially available FDA‐approved DM/DBT systems, which implement both direct and indirect detector technology. The updated versions of the MC‐GPU codes that can be used to replicate three systems are available in open source format through GitHub.

Sengupta, Aunnasha↗

DOE OSTI2023

The Reductive Leaching of Chalcopyrite by Chromium(II) Chloride for the Rapid and Complete Extraction of Copper

A hydrometallurgical process is developed to lower the costs of copper production and thereby sustain the use of copper throughout the global transition to renewable energy technologies. The unique feature of the hydrometallurgical process is the reductive treatment of chalcopyrite, which is in contrast to the oxidative treatment more commonly pursued in the literature. Chalcopyrite reduction by chromium(II) ion is described for the first time and superior kinetics are shown. At high concentrate loadings of 39, 78, and 117 gL –1 , chalcopyrite reacted completely within minutes at room temperature and pressure. The XRD, SEM-EDS, and XPS measurements indicate that chalcopyrite reacts to form copper(I) chloride (CuCl). After the reductive treatment, the mineral products are leached by iron(III) sulfate to demonstrate the complete extraction of copper. The chromium(II) ion may be regenerated by an electrolysis unit inspired by an iron chromium flow battery in a practical industrial process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Engineered selective biotoxin‐binding hydrogels for toxin sequestration

The development of synthetic selective membranes that separate materials of similar sizes, charges, and/or polarities remains a difficult challenge, and looking towards biology provides inspiration for new designs. In this work, a series of cholera toxin binding peptides (CTBPs) are identified, spanning a range of binding inhibitions, and integrated into chemically cross-linked cholera toxin binding gels (CTBGs) via thiol-Michael polycondensation reactions. All gels demonstrate rheological profiles consistent with elastic solids. The CTBGs are probed via small-angle neutron scattering and exhibit a correlation length, ξ, smaller than most proteins (1.3–2.5 nm). Thus, an effective entropic mesh is formed to block non-targeted proteins. However, the CTBGs have a dynamic mesh size, Ξ, that is larger than cholera toxin (CT) to allow the transport of target proteins. The CTBGs with the highest binding inhibitions both show high selectivity and permeation of CT, rejecting all other tested proteins. In total, two new highly selective CTBGs are synthesized and validated for use in cholera toxin remediation. Together, this platform demonstrates the wide applicability of selectively-diffusive materials for difficult separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Quasi-elastic neutron scattering study on dynamically asymmetric polymer blends

Compatibility between polymers with different glass transition temperatures controls the thermomechanical properties of blends. This work explores the segmental dynamics of poly(methyl acrylate) (PMA) chains when they are blended with polymers of different rigidities and miscibility. Inspired by the intriguing dynamic asymmetry of chains within the interfacial layer of nanoparticles, this study aims to understand dynamic heterogeneity in dynamically asymmetric blends of PMA/poly(methyl methacrylate) (PMMA), PMA/polystyrene (PS), and PMA/poly(ethylene oxide) (PEO) using the differential scanning calorimetry (DSC) and quasi-elastic neutron scattering (QENS) measurements below and above the glass transition temperature of PMMA or PS. Further, results revealed that the segmental jump distance of PMA increased on blending due to volume enhancement. The reduced effective diffusivity of PMA in PMMA is attributed to PMMA's enhanced flexibility (lower characteristic ratio, C ∞ ) and superior interaction (lower ) when compared with the PS environment. The results demonstrate that the chain rigidity and miscibility affect the free volume, interchain cooperativity, and segmental dynamics in dynamically asymmetric blends.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Hierarchical, rotation‐equivariant neural networks to select structural models of protein complexes

Abstract Predicting the structure of multi‐protein complexes is a grand challenge in biochemistry, with major implications for basic science and drug discovery. Computational structure prediction methods generally leverage predefined structural features to distinguish accurate structural models from less accurate ones. This raises the question of whether it is possible to learn characteristics of accurate models directly from atomic coordinates of protein complexes, with no prior assumptions. Here we introduce a machine learning method that learns directly from the 3D positions of all atoms to identify accurate models of protein complexes, without using any precomputed physics‐inspired or statistical terms. Our neural network architecture combines multiple ingredients that together enable end‐to‐end learning from molecular structures containing tens of thousands of atoms: a point‐based representation of atoms, equivariance with respect to rotation and translation, local convolutions, and hierarchical subsampling operations. When used in combination with previously developed scoring functions, our network substantially improves the identification of accurate structural models among a large set of possible models. Our network can also be used to predict the accuracy of a given structural model in absolute terms. The architecture we present is readily applicable to other tasks involving learning on 3D structures of large atomic systems.

Eismann, Stephan↗

DOE OSTI2021

Improving protein tertiary structure prediction by deep learning and distance prediction in CASP14

Abstract Substantial progresses in protein structure prediction have been made by utilizing deep‐learning and residue‐residue distance prediction since CASP13. Inspired by the advances, we improve our CASP14 MULTICOM protein structure prediction system by incorporating three new components: (a) a new deep learning‐based protein inter‐residue distance predictor to improve template‐free (ab initio) tertiary structure prediction, (b) an enhanced template‐based tertiary structure prediction method, and (c) distance‐based model quality assessment methods empowered by deep learning. In the 2020 CASP14 experiment, MULTICOM predictor was ranked seventh out of 146 predictors in tertiary structure prediction and ranked third out of 136 predictors in inter‐domain structure prediction. The results demonstrate that the template‐free modeling based on deep learning and residue‐residue distance prediction can predict the correct topology for almost all template‐based modeling targets and a majority of hard targets (template‐free targets or targets whose templates cannot be recognized), which is a significant improvement over the CASP13 MULTICOM predictor. Moreover, the template‐free modeling performs better than the template‐based modeling on not only hard targets but also the targets that have homologous templates. The performance of the template‐free modeling largely depends on the accuracy of distance prediction closely related to the quality of multiple sequence alignments. The structural model quality assessment works well on targets for which enough good models can be predicted, but it may perform poorly when only a few good models are predicted for a hard target and the distribution of model quality scores is highly skewed. MULTICOM is available at https://github.com/jianlin-cheng/MULTICOM_Human_CASP14/tree/CASP14_DeepRank3 and https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Synthesis, aphicidal activity and conformation of novel insect kinin analogues as potential eco‐friendly insecticides

Abstract BACKGROUND The discovery of ecofriendly insecticides through a new strategy for aphid control is important because of the substantial resistance and unexpected eco‐toxicity to honeybees caused by traditional insecticides. The insect kinins, a class of multifunctional insect neuropeptides, are considered for potential application in pest control. In our previous work we developed several series of insect kinin analogues and found a promising lead II‐1 with good aphicidal activity. To seek further eco‐friendly aphicides, the optimization of II‐1 is carried out in this study. RESULTS Fifteen novel Yaa 3 modified analogues based on the lead II‐1 were synthesized. The aphicidal tests indicated that IV‐3 , IV‐5 and IV‐10 exhibited significant activity against the soybean aphid Aphis glycines with LC 50 values of 0.0029, 0.0072 and 0.0086 mmol L –1 , respectively, higher than that of lead II‐1 and the commercial Pymetrozine. The molecular modeling results showed that analogues II‐1 , IV‐3 , IV‐5 , IV‐7 and IV‐10 formed a β‐turn‐like conformation, while the conformation of analogues IV‐1 , IV‐2 and IV‐9 seemed to be linear. Some structural elements favorable for the activity were proposed based on the conformation‐activity relationship of the analogues. CONCLUSION Insect kinin analogues derived from lead II‐1 by modifying the hydrolysis site Yaa 3 with natural, sterically hindered α‐ and β‐amino acids showed great potential as eco‐friendly insecticides. Inspiringly, the most active analogue IV‐3 can be a candidate for further development. The β‐turn‐like conformation and the orientation of the aromatic rings of the side chain of Phe 2 and Trp 4 may be critical factors beneficial to activity. © 2019 Society of Chemical Industry

Zhang, Chuanliang↗