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The vast number of element combinations and the explosive growth of composition space pose significant challenges to the development of high‐entropy alloys (HEAs). Here, we propose a procedural research method aimed at accelerating the discovery of efficient electrocatalysts for oxygen reduction reaction (ORR) based on Pt‐based quinary HEAs. The method begins with an element library provided by a large language model (LLM), combined with microscale precursor printing and pulse high‐temperature synthesis techniques to prepare multi‐element combination HEA array in one step. Through high‐throughput measurement using scanning electrochemical cell microscopy (SECCM), precise identification of highly active HEA element combinations and exploration of composition space for a specific combination are achieved. Advantageous element combinations are further validated in practical electrocatalytic evaluations. The contributions of individual element sites and the synergistic effects among elements of such HEAs in enhancing reaction activity are elucidated via density functional theory (DFT) calculations. This method integrates high‐throughput experiments, practical catalyst validation, and DFT calculations, providing a new pathway for accelerating the discovery of efficient multi‐element materials in the field of energy catalysis. [ABSTRACT FROM AUTHOR]

Na–Fe–Mn–O cathodes hold promise for environmentally benign high‐energy sodium‐ion batteries, addressing material scarcity concerns in Li‐ion batteries. To date, these materials show poor stability in the air and suffer significant Fe/Mn dissolution during use. These two detrimental surface effects have so far prevented the commercialization of these materials. Herein, high‐throughput experiments to make hundreds of substitutions into a previously optimized Na–Fe–Mn–O material are utilized. Numerous single‐phase materials are made with good electrochemical performance that shows moderate improvements over the unsubstituted. By contrast, dramatic improvements are made in suppressing decomposition in air and Fe/Mn dissolution. Machine learning algorithms are utilized to further understand the changes in air stability and to decouple the effects of various structural parameters such as lattice parameters and crystallite size. The comprehensive dataset and methodology established here lay the groundwork for future exploration and optimization of cathode materials, driving the advancement of next‐generation sodium‐ion batteries. [ABSTRACT FROM AUTHOR]

Using High‐Throughput Experimentation (HTE), a visible light‐mediated etherification method was developed, employing the organic photocatalyst thioxanthen‐9‐one (TXO) and a commercially available, air‐stable nickel source. Design of Experiments (DoE) techniques were utilized in conjunction with HTE to identify optimal reaction conditions which are mild, cost‐effective, robust and therefore suitable for use in an industrial setting. A diverse substrate scope was prepared via parallel synthesis and selected examples were demonstrated on a 4.2 mmol scale in batch. Furthermore, the reaction was successfully performed on an 83 mmol scale, utilizing a standard jacketed reactor and Kessil PR160 L lamps. [ABSTRACT FROM AUTHOR]

High‐throughput experimentation, a well‐established and powerful tool in the field of heterogeneous catalysis screening, has been extended to the field of electrochemistry. A parallel and modular high‐throughput screening platform was designed, involving the development of a new modular electrochemical flow cell by the Fraunhofer Institute for Microengineering and Microsystems (IMM) in cooperation with hte GmbH and IEK‐9 / FZ Jülich GmbH. Here, this platform is introduced for alkaline water electrolysis, showing initial results that underline the flexibility, comparability, and accuracy of the experiments. [ABSTRACT FROM AUTHOR]

Na–Fe–Mn–O cathodes hold promise for environmentally benign high‐energy sodium‐ion batteries, addressing material scarcity concerns in Li‐ion batteries. To date, these materials show poor stability in the air and suffer significant Fe/Mn dissolution during use. These two detrimental surface effects have so far prevented the commercialization of these materials. Herein, high‐throughput experiments to make hundreds of substitutions into a previously optimized Na–Fe–Mn–O material are utilized. Numerous single‐phase materials are made with good electrochemical performance that shows moderate improvements over the unsubstituted. By contrast, dramatic improvements are made in suppressing decomposition in air and Fe/Mn dissolution. Machine learning algorithms are utilized to further understand the changes in air stability and to decouple the effects of various structural parameters such as lattice parameters and crystallite size. The comprehensive dataset and methodology established here lay the groundwork for future exploration and optimization of cathode materials, driving the advancement of next‐generation sodium‐ion batteries.

The alkyne motif is present in several naturally occurring molecules and specialty chemicals. The Sonogashira reaction is recognized as one of the most exploited methods for the synthesis of substituted alkynes. While palladium catalysts have been traditionally employed, recent efforts have been directed towards developing alternatives based on Earth‐abundant metals such as nickel. However, nickel‐catalyzed Sonogashira couplings still present important substrate limitations. While more reactive aryl iodides have been well documented, the coupling of more challenging yet cost‐effective aryl bromides and chlorides lags significantly behind. Herein, we present a method for the nickel‐catalyzed Sonogashira coupling of aryl bromides and chlorides, whose discovery was accelerated thanks to high‐throughput experimentation. Fine‐tuning of the bipyridine ligand was essential to activate chemoselectively C(sp2)−Br or C(sp2)−Cl bonds. A wide range of functionally diverse alkynes, including examples of active ingredients and their intermediates, were accessed in a single step and with yields up to 98%. The developed methodology is expected to be widely adopted by academia and industry, marking a step forward in the field of base metal catalysis. [ABSTRACT FROM AUTHOR]

The emergence of High-Throughput Experimentation (HTE) as a powerful tool for reaction discovery and optimization is changing the way organic chemists are designing their experiments. It is a fantastic way to largely investigate a reaction, in a minimum of time and reagent consumption. However, HTE needs to be accessible to a wide audience for a full implementation in academic and industrial sectors. In that context, developing accessible solid dosing methodologies for submilligram dispensing is necessary. This paper aims at proposing robust mass enhancers solutions for nanomole scale dosing applicable to HTE campaigns. [ABSTRACT FROM AUTHOR]

The development of new polymer materials is an emerging field for more than 100 years. However, it is currently facing major challenges and the application of digital methods can help to develop new processes, discover new materials and, thus, contribute to the challenges of today and the future. Though, the application of digital methods in the field of polymer science is currently very limited, when compared to other classes of materials such as small molecules or inorganic high‐performance materials. Nevertheless, there are already first, very promising approaches. The current review article focuses on the application of these methods in different aspects of polymer research including polymer design, synthesis, and characterization. Furthermore, the digital discovery of polymer engineering is highlighted in detail showing the broad range of potential applications of these methods in polymer science. Finally, future possibilities and opportunities are derived from the current state‐of‐the‐art and perspectives for a potential evolution of polymer science are provided. [ABSTRACT FROM AUTHOR]

Alloying dissimilar elements presents an effective strategy for enhancing the electrocatalytic properties of multi‐metal materials. This enhancement can be attributed to the modification of electronic and geometric effects, which play a crucial role in determining the overall electrocatalytic performance. However, these effects are intricately intertwined and often interrelated due to their coexistence. As a result, the improved catalytic performance of multi‐metal systems is frequently attributed to synergistic or "cocktail" effects, without clear explanations of the role of alloying and the individual contribution of each element. A high‐throughput experimentation approach is employed to investigate 342 compositions within the quaternary thin film system Pd─Ag─Cu─Fe. The substitution of Cu with Fe (different number of valence electrons) or Ag (different atomic sizes) allows for selective manipulation of electronic or geometric effects, respectively. The substitution of Ag with Fe allows for the simultaneous variation of both effects. The number of valence electrons per unit cell volume is used as a descriptor for electrocatalytic activity, specifically with respect to the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), which can be optimized through independent or simultaneous alteration of electronic and geometric effects. [ABSTRACT FROM AUTHOR]

The photochemical thiol‐ene reaction is an efficient method for rapid and chemoselective formation of thioether linkages under mild conditions. It has found widespread use in small‐molecule synthesis as well as peptide and protein chemistry. While high‐throughput experimentation provides an invaluable tool for drug discovery, the considerable potential of the thiol‐ene reaction remains unexplored in this context. Herein, we report the development of nanomole‐scale photochemical thiol‐ene chemistry, performed using an automated approach in 1536‐well plates in a cost‐efficient custom reactor. Through careful reaction design and selection of reactants, this chemistry is applied to the lateral diversification of peptidic macrocycles to yield high purity crude mixtures. Selection of the photoinitiator 2,2‐dimethoxy‐2‐phenylacetophenone yields volatile breakdown products, which facilitates removal in vacuo. We demonstrate the use of this approach in late‐stage diversification of peptidic macrocycles with 96 examples averaging 95% conversion to the desired product. [ABSTRACT FROM AUTHOR]

Evaluation of the relative rates of the cobalt‐catalyzed C(sp2)–C(sp3) Suzuki–Miyaura cross‐coupling between the neopentylglycol ester of 4‐fluorophenylboronic acid and N‐Boc‐4‐bromopiperidine established that smaller N‐alkyl substituents on the phenoxyimine (FI) supporting ligand accelerated the overall rate of the reaction. This trend inspired the design of optimal cobalt catalysts with phenoxyoxazoline (FOx) and phenoxythiazoline (FTz) ligands. An air‐stable cobalt(II) precatalyst, (FTz)CoBr(py)3 was synthesized and applied to the cross‐coupling of an indole‐5‐boronic ester nucleophile with a piperidine‐4‐bromide electrophile that is relevant to the synthesis of reported toll‐like receptor (TLR) 7/8 antagonist molecules including afimetoran. Addition of excess KOMe⋅B(OiPr)3 improved catalyst lifetime due to attenuation of alkoxide basicity that otherwise resulted in demetallation of the FI chelate. A first‐order dependence on the cobalt precatalyst and a saturation regime in nucleophile were observed, supporting turnover‐limiting transmetalation and the origin of the observed trends in N‐imine substitution. [ABSTRACT FROM AUTHOR]

Research towards the development of novel synthetic methods to access substituted bicyclo[1.1.1]pentane (BCP) structural motifs has been conducted by both academic groups and industrial organizations. Recent developments have been strongly focused on the utility of visible light catalysis to promote a cornucopia of radical‐based transformations, including incorporation of BCP motifs. While these methods have proven powerful in accessing various substitution patterns, some scalability challenges remain. Herein we describe a focused effort on the high‐throughput experimentation (HTE) guided optimization of a decarboxylative non‐photonic coupling that can be conducted using traditional batch reactors. Employing an unanticipated mixture of copper(I) chloride and cyclopentyl methyl ether (CPME) results in the formation of a N‐substituted bicyclo[1.1.1]pentyl pyrazole product while limiting the overall equivalency of the hypervalent iodonium precursor. [ABSTRACT FROM AUTHOR]

A novel and convenient approach that combines high‐throughput experimentation (HTE) with machine learning (ML) technologies to achieve the first selective cross‐dimerization of sulfoxonium ylides via iridium catalysis is presented. A variety of valuable amide‐, ketone‐, ester‐, and N‐heterocycle‐substituted unsymmetrical E‐alkenes are synthesized in good yields with high stereoselectivities. This mild method avoids the use of diazo compounds and is characterized by simple operation, high step‐economy, and excellent chemoselectivity and functional group compatibility. The combined experimental and computational studies identify an amide‐sulfoxonium ylide as a carbene precursor. Furthermore, a comprehensive exploration of the reaction space is also performed (600 reactions) and a machine learning model for reaction yield prediction has been constructed. [ABSTRACT FROM AUTHOR]

Introducing amide functional groups under mild conditions has growing importance owing to the prevalence of such moiety in biologically active molecules. Herein, we disclose a mild protocol for the directed ruthenium‐catalyzed C−H aminocarbonylation with isocyanates as the amidating agents developed through high‐throughput experimentation (HTE). The redox‐neutral and base‐free reaction is guided by weakly Lewis basic functional groups, including anilides, lactams and carbamates to access anthranilamide derivatives. The synthetic utility of this transformation is reflected by large‐scale synthesis and late‐stage functionalization. [ABSTRACT FROM AUTHOR]

Within the realm of drug discovery, high‐throughput experimentation techniques enable the rapid optimization of reactions and expedited generation of drug compound libraries for biological and pharmacokinetic evaluation. Herein we report the development of a segmented flow mass spectrometry‐based platform to enable the rapid exploration of photoredox reactions for early‐stage drug discovery. Specifically, microwell plate‐based photochemical reaction screens were reformatted to segmented flow format to enable delivery to nanoelectrospray ionization‐mass spectrometry analysis. This approach was demonstrated for the late‐stage modification of complex drug scaffolds, as well as the subsequent structure–activity relationship evaluation of synthesized analogs. This technology is anticipated to expand the robust capabilities of photoredox catalysis in drug discovery by enabling high‐throughput library diversification. [ABSTRACT FROM AUTHOR]

The rapid development of biologics and vaccines in response to the current pandemic has highlighted the need for robust platform assays to characterize diverse biopharmaceuticals. A critical aspect of biopharmaceutical development is achieving a highly pure product, especially with respect to residual host cell material. Specifically, two important host cell impurities of focus within biopharmaceuticals are residual DNA and protein. In this work, a novel high-throughput host cell DNA quantitation assay was developed for rapid screening of complex vaccine drug substance samples. The developed assay utilizes the commercially available, fluorescent-sensitive Picogreen dye within a 96-well plate configuration to allow for a cost effective and rapid analysis. The assay was applied to in-process biopharmaceutical samples with known interferences to the dye, including RNA and protein. An enzymatic digestion pre-treatment was found to overcome these interferences and thus allow this method to be applied to wide-ranging, diverse analyses. In addition, the use of deoxycholate in the digestion treatment allowed for disruption of interactions in a given sample matrix in order to more accurately and selectively quantitate DNA. Critical analytical figures of merit for assay performance, such as precision and spike recovery, were evaluated and successfully demonstrated. This new analytical method can thus be successfully applied to both upstream and downstream process analysis for biologics and vaccines using an innovative and automated high-throughput approach. [ABSTRACT FROM AUTHOR]

The sulfur fluoride exchange (SuFEx) reaction is significant in drug discovery, materials science, and chemical biology. Conventionally, it involves installation of SO2F followed by fluoride exchange by a catalyst. We report catalyst‐free Aza‐Michael addition to install SO2F and then SuFEx reaction with amines, both occurring in concert, in microdroplets under ambient conditions. The microdroplet reaction is accelerated by a factor of ∼104 relative to the corresponding bulk reaction. We suggest that the superacidic microdroplet surface assists SuFEx reaction by protonating fluorine to create a good leaving group. The reaction scope was established by performing individual reactions in microdroplets of 18 amines in four solvents and confirmed using high‐throughput desorption electrospray ionization experiments. The study demonstrates the value of microdroplet‐assisted accelerated reactions in combination with high‐throughput experimentation for characterization of reaction scope. [ABSTRACT FROM AUTHOR]

The development of a scalable and highly reproducible in vitro tumor microenvironment (TME) platform still sheds light on new insights into cancer metastasis mechanisms and anticancer therapeutic strategies. Here, we present an all‐in‐one injection molded plastic array three‐dimensional culture platform (All‐in‐One‐IMPACT) that integrates vascularized tumor spheroids for highly reproducible, high‐throughput experimentation. This device allows the formation of self‐assembled cell spheroids on a chip by applying the hanging drop method to the cell culture channel. Then, when the hydrogel containing endothelial cells and fibroblasts is injected, the spheroid inside the droplet can be patterned together in three dimensions along the culture channel. In just two steps above, we can build a vascularized TME within a defined area. This process does not require specialized user skill and minimizes error‐inducing steps, enabling both reproducibility and high throughput of the experiment. We have successfully demonstrated the process, from spheroid formation to tumor vascularization, using patient‐derived cancer cells (PDCs) as well as various cancer cell lines. Furthermore, we performed combination therapies with Taxol (paclitaxel) and Avastin (bevacizumab), which are used in standard care for metastatic cancer. The All‐in‐One IMPACT is a powerful tool for establishing various anticancer treatment strategies through the development of a complex TME for use in high‐throughput experiments. [ABSTRACT FROM AUTHOR]

High-entropy alloys (HEAs) present both significant potential and challenges for developing efficient electrocatalysts due to their diverse combinations and compositions. Here, we propose a procedural approach that combines high-throughput experimentation with data-driven strategies to accelerate the discovery of efficient HEA electrocatalysts for the hydrogen evolution reaction (HER). This enables the rapid preparation of HEA arrays with various element combinations and composition ratios within a model system. The intrinsic activity of the HEA arrays is swiftly screened using scanning electrochemical cell microscopy (SECCM), providing precise composition-activity data sets for the HEA system. An ensemble machine learning (EML) model is then used to predict the activity database for the composition subspace of the system. Based on these database results, two groups of promising catalysts are recommended and validated through actual electrocatalytic evaluations. This procedural approach, which combines high-throughput experimentation with data-driven strategies, provides a new pathway to accelerate the discovery of efficient HEA electrocatalysts.

Combinatorial spread libraries offer an approach to explore the evolution of material properties over broad concentration, temperature, and growth parameter spaces. However, the traditional limitation of this approach is the requirement for the read-out of functional properties across the library. Here we develop automated piezoresponse force microscopy (PFM) for the exploration of combinatorial spread libraries and demonstrate its application in the Sm x Bi 1- x FeO 3 system with the ferroelectric-antiferroelectric morphotropic phase boundary. This approach relies on the synergy of the quantitative nature of PFM and the implementation of automated experiments that allow PFM-based sampling of macroscopic samples. The concentration dependence of pertinent ferroelectric parameters was determined and used to develop the mathematical framework based on the Ginzburg-Landau theory describing the evolution of these properties across the concentration space. We pose that a combination of automated scanning probe microscope and combinatorial spread library approach will emerge as an efficient research paradigm to close the characterization gap in high-throughput materials discovery. We make the data sets open to the community, and we hope that this will stimulate other efforts to interpret and understand the physics of these systems.

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure-function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.

Direct reductive amination (DRA) is a ubiquitous reaction in organic chemistry. This transformation between a carbonyl group and an amine is most often achieved by using a super stoichiometric amount of hazardous hydride reagents, thus being incompatible with many sensitive functional groups. DRA could also be achieved by means of chemo‐ or biocatalysis, thereby attracting the interest of industry as well as academic laboratories due to the virtually perfect atom economy. Although DRAs are well‐established for substrate pairs such as aldehydes with either 1° or 2° amines as well as ketones with 1° amines, the current methodologies are limited in the case of ketones with 2° amines. Herein, we present a general DRA protocol that overcomes this major limitation by means of iridium catalysis. The applicability of the methodology is demonstrated by accessing an unprecedented range of biologically relevant tertiary amines starting from both aliphatic ketones and aliphatic amines. The choice of a disphosphane ligand (Josiphos A or Xantphos) is essential for the success of the transformation. [ABSTRACT FROM AUTHOR]

Efficient and economical plastic waste upcycling relies on the development of catalysts capable of polymer degradation. A systematic high‐throughput screening of twenty‐eight polymerization catalyst precursors, belonging to the catalyst families of metallocenes, ansa‐metallocenes, and hemi‐ and post‐metallocenes, in cis‐1,4‐polybutadiene (PB) degradation reveals, for the first time, important structure–activity correlations. The upcycling conditions involve activation of the catalysts (at 0.18 % catalyst loading) with tri‐iso‐butyl aluminum at 50 °C in toluene. The data indicate the ability to degrade PB is a general reactivity profile of neutral group 4 metal hydrides. A simple quantitative‐structure activity relationship (QSAR) model utilizing two descriptors for the distribution of steric bulk in the active pocket and one measuring the metal ion electrophilicity reveals the degradation ability improves with increased but not overbearing steric congestion and lower electrophilicity. [ABSTRACT FROM AUTHOR]

The timely arrival of novel materials plays a key role in bringing advances to society, as the pace at which major technological breakthroughs take place is usually dictated by the discovery rate at which novel materials are identified within chemical space. High‐throughput experimentation and computation strategy, now widely considered as a watershed in accelerating the discovery and optimization of novel materials in virtually every field, enables simultaneous screening, synthesis and characterization of large arrays of different material classes toward identification of the lead candidates for given system and targeted application. However, the ability to acquire data, through the continued advancement of automation platforms and workflows especially in the field of battery research and development, often outpaces the ability to optimally leverage obtained data for improved decision‐making. Closing this gap inevitably calls for adapted algorithms, development of reliable predictive models and enhanced integration with machine learning, deep learning, and artificial intelligence. This Review aims to highlight state‐of‐the‐art achievements along with an assessment of current and future challenges as well as resulting perspectives toward accelerated development of advanced battery electrolytes and their interfaces. [ABSTRACT FROM AUTHOR]

[Display omitted] A novel approach based on supervised machine-learning is proposed to predict the solubility of drugs and drug-like molecules in mixtures of organic solvents. Similar to quantitative structure–property relationship (QSPR) models, different solvent types are identified by molecular descriptors, which, in this study, are considered as UNIFAC subgroups. To overcome the potential lack of UNIFAC subgroups for the complex Active Pharmaceutical Ingredients (APIs) currently developed in the pharmaceutical industry, the API molecule is considered as a unique entity in the proposed modelling approach. Therefore, API solubility is predicted as a function of temperature, functional subgroups of the solvents and composition of the solvent mixture; in turn, regressors' correlation is handled through Partial Least-Squares (PLS) regression. The method is developed and tested with experimental data of a real API and 14 organic solvents that are industrially employed for crystallisation. Solubility predictions are accurate and precise for single solvents, binary mixtures and ternary mixtures of organic solvents at different compositions and temperatures, with a determination coefficient R 2 ≥ 0.90. To further test the applicability of the model, the proposed approach is applied to 9 literature organic solubility datasets of drugs and drug-like compounds and compared to benchmark solubility models in the literature. Results show that the proposed approach provides satisfactory predictions: the majority of validation and calibration data have R 2 = 0.95–0.99; the ratio between RMSE (root mean squared error) of the proposed method and the range of measured solubility values is from 1 to 3 orders of magnitude smaller than the RMSE ratio obtained by the benchmark models. [ABSTRACT FROM AUTHOR]

• B-spline descriptors are engineered purely from absorbance spectra. • Two-step workflow improves conductivity measurement efficiency significantly. • Conductivity classifier attains a maximum of 100 % accuracy. • Conductivity regressor attains an R2 of 0.984. • Explainable ML reveals spectral influence on doped polymers' conductivity. The combination of high-throughput experimentation techniques and machine learning (ML) has recently ushered in a new era of accelerated material discovery, enabling the identification of materials with cutting-edge properties. However, the measurement of certain physical quantities remains challenging to automate. Specifically, meticulous process control, experimentation and laborious measurements are required to achieve optimal electrical conductivity in doped polymer materials. We propose a ML approach, which relies on readily measured absorbance spectra, to accelerate the workflow associated with measuring electrical conductivity. The classification model accurately classifies samples with a conductivity >∼25 to 100 S/cm, achieving a maximum of 100 % accuracy rate. For the subset of highly conductive samples, we employed a regression model to predict their conductivities, yielding an impressive test R2 value of 0.984. We tested the models with samples of the two highest conductivities (498 and 506 S/cm) and showed that they were able to correctly classify and predict the two extrapolative conductivities at satisfactory levels of errors. The proposed ML-assisted workflow results in an improvement in the efficiency of the conductivity measurements by 89 % of the maximum achievable using our experimental techniques. Furthermore, our approach addressed the common challenge of the lack of explainability in ML models by exploiting bespoke mathematical properties of the descriptors and ML model, allowing us to gain corroborated insights into the spectral influences on conductivity. Through this study, we offer an accelerated pathway for optimizing the properties of doped polymer materials while showcasing the valuable insights that can be derived from purposeful utilization of ML in experimental science. [ABSTRACT FROM AUTHOR]

Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors' knowledge, is demonstrated for the first time herein. [ABSTRACT FROM AUTHOR]

High entropy alloys (HEA) comprise a huge search space for new electrocatalysts. Next to element combinations, the optimization of the chemical composition is essential for tuning HEA to specific catalytic processes. Simulations of electrocatalytic activity can guide experimental efforts. Yet, the currently available underlying model assumptions do not necessarily align with experimental evidence. To study deviations of theoretical models and experimental data requires statistically relevant datasets. Here, a combinatorial strategy for acquiring large experimental datasets of multi‐dimensional composition spaces is presented. Ru–Rh–Pd–Ir–Pt is studied as an exemplary, highly relevant HEA system. Systematic comparison with computed electrochemical activity enables the study of deviations from theoretical model assumptions for compositionally complex solid solutions in the experiment. The results suggest that the experimentally obtained distribution of surface atoms deviates from the ideal distribution of atoms in the model. Leveraging both advanced simulation and large experimental data enables the estimation of electrocatalytic activity and solid‐solution stability trends in the 5D composition space of the HEA system. A perspective on future directions for the development of active and stable HEA catalysts is outlined. [ABSTRACT FROM AUTHOR]

The combination of high-throughput experimentation (HTE) and data analysis is a valuable methodology for mechanistic interrogation and rational development of catalysts. In this article, we point out the general structure of HTE-data analysis workflow and illustrate how it can be applied with examples of olefin metathesis and cyanation reactions.

Electrochemistry is quickly entering the mainstream of synthetic organic chemistry. The diversity of new transformations enabled by electrochemistry is to a large extent a consequence of the unique features and reaction parameters in electrochemical systems including redox mediators, applied potential, electrode material, and cell construction. While offering chemists new means to control reactivity and selectivity, these additional features also increase the dimensionalities of a reaction system and complicate its optimization. This challenge, however, has spawned increasing adoption of data science tools to aid reaction discovery as well as development of high-throughput screening platforms that facilitate the generation of high quality datasets. In this Perspective, we provide an overview of recent advances in data-science driven electrochemistry with an emphasis on the opportunities and challenges facing this growing subdiscipline.

An efficient photoanode is a prerequisite for a viable solar fuels technology. The challenges to realizing an efficient photoanode include the integration of a semiconductor light absorber and a metal oxide electrocatalyst to optimize corrosion protection, light trapping, hole transport, and photocarrier recombination sites. To efficiently explore metal oxide coatings, we employ a high-throughput methodology wherein a uniform BiVO4 film is coated with 858 unique metal oxide coatings covering a range of metal oxide loadings and the full (Ni-Fe-Co-Ce)Ox pseudoquaternary composition space. Photoelectrochemical characterization of the photoanodes reveals that specific combinations of metal oxide composition and loading provide up to a 13-fold increase in the maximum photoelectrochemical power generation for oxygen evolution in pH 13 electrolyte. Through mining of the high-throughput data we identify composition regions that form improved interfaces with BiVO4. Of particular note, integrated photoanodes with catalyst compositions in the range Fe(0.4-0.6)Ce(0.6-0.4)Ox exhibit high interface quality and excellent photoelectrochemical power conversion. Scaled-up inkjet-printed electrodes and photoanodic electrodeposition of this composition on BiVO4 confirms the discovery and the synthesis-independent interface improvement of (Fe-Ce)Ox coatings on BiVO4.

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of Saccharomyces cerevisiae by Gander et al. Our experimental campaign used a novel high-throughput experimentation framework developed under Defense Advanced Research Projects Agency's Synergistic Discovery and Design program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report and make some remarks about lessons learned. Graphical Abstract [ABSTRACT FROM AUTHOR]

The functionalization of C−H bonds, ubiquitous in drugs and drug‐like molecules, represents an important synthetic strategy with the potential to streamline the drug‐discovery process. Late‐stage aromatic C−N bond–forming reactions are highly desirable, but despite their significance, accessing aminated analogues through direct and selective amination of C−H bonds remains a challenging goal. The method presented herein enables the amination of a wide array of benzoic acids with high selectivity. The robustness of the system is manifested by the large number of functional groups tolerated, which allowed the amination of a diverse array of marketed drugs and drug‐like molecules. Furthermore, the introduction of a synthetic handle enabled expeditious access to targeted drug‐delivery conjugates, PROTACs, and probes for chemical biology. This rapid access to valuable analogues, combined with operational simplicity and applicability to high‐throughput experimentation has the potential to aid and considerably accelerate drug discovery. [ABSTRACT FROM AUTHOR]

Homogeneous hydrogenation catalysts based on metal complexes provide a diverse and highly tunable tool for the fine chemical industry. To fully unleash their potential, fast and effective methods for the evaluation of catalytic properties are needed. In turn, this requires changes in the experimental approaches to test and evaluate the performance of the catalytic processes. Design of experiment combined with statistical analysis can enable time‐ and resource‐efficient experimentation. In this work, we employ a set of different statistical models to obtain the detailed kinetic description of a highly active homogeneous Mn (I) ketone hydrogenation catalyst as a representative model system. The reaction kinetics were analyzed using a full second order polynomial regression model, two models with eliminated parameters and finally a model which implements "chemical logic". The coefficients obtained are compared with the corresponding high‐quality kinetic parameters acquired using conventional kinetic experiments. We demonstrate that various kinetic effects can be well captured using different statistical models, providing important insights into the reaction kinetics and mechanism of a complex catalytic reaction. [ABSTRACT FROM AUTHOR]

A synthetic method for the palladium‐catalyzed cyanation of aryl boronic acids using bench stable and non‐toxic N‐cyanosuccinimide has been developed. High‐throughput experimentation facilitated the screen of 90 different ligands and the resultant statistical data analysis identified that ligand σ‐donation, π‐acidity and sterics are key drivers that govern yield. Categorization into three ligand groups – monophosphines, bisphosphines and miscellaneous – was performed before the analysis. For the monophosphines, the yield of the reaction increases for strong σ‐donating, weak π‐accepting ligands, with flexible pendant substituents. For the bisphosphines, the yield predominantly correlates with ligand lability. The applicability of the designed reaction to a wider substrate scope was investigated, showing good functional group tolerance in particular with boronic acids bearing electron‐withdrawing substituents. This work outlines the development of a novel reaction, coupled with a fast and efficient workflow to gain understanding of the optimal ligand properties for the design of improved palladium cross‐coupling catalysts. [ABSTRACT FROM AUTHOR]

We have designed an open-source and high-throughput thermal analysis system for fast and accurate estimation of phase transition temperatures for up to 96 samples analyzed simultaneously. The results are comparable to current state of the art systems such as differential scanning calorimetry (DSC), which is costly and time-consuming. The PhasIR hardware system utilizes an infrared camera to optically record thermal processes that result from the melting/freezing and/or evaporation of samples, while the accompanying open-source software package allows for subsequent batch analysis and processing to extract phase transition temperatures. The PhasIR system showed good agreement with DSC results for both pure substances and mixtures, such as deep eutectic solvents (DES). The all-in-one hardware and software system can be easily replicated and built using relatively inexpensive components at a total estimated cost of $1,080 USD. Implementation of the PhasIR system will allow for increased throughput in material thermal characterization and broader investigation of material design spaces. Metadata Overview Main design files: https://github.com/pozzo-research-group/phasIR Target group: Scientists requiring high-throughput thermal analysis of organic samples (e.g. pharmaceuticals, polymers, food ingredients, greases, solvents). Skills required: CNC machining, 3D printing, basic electronics and programming. Replication: No builds known to the authors so far.

While high-throughput (HT) experimentation and mechanistic modeling have long been employed in chromatographic process development, it remains unclear how these techniques should be used in concert within development workflows. In this work, a process development workflow based on HT experiments and mechanistic modeling was constructed. The integration of HT and modeling approaches offers improved workflow efficiency and speed. This high-throughput in silico (HT-IS) workflow was employed to develop a Capto MMC polishing step for mAb aggregate removal. High-throughput batch isotherm data was first generated over a range of mobile phase conditions and a suite of analytics were employed. Parameters for the extended steric mass action (SMA) isotherm were regressed for the multicomponent system. Model validation was performed using the extended SMA isotherm in concert with the general rate model of chromatography using the CADET modeling software. Here, step elution profiles were predicted for eight RoboColumn runs across a range of ionic strength, pH, and load density. Optimized processes were generated through minimization of a complex objective function based on key process metrics. Processes were evaluated at lab-scale using two feedstocks, differing in composition. The results confirmed that both processes obtained high monomer yield (>85%) and removed ∼ 50 % $$ \sim 50\% $$ of aggregate species. Column simulations were then carried out to determine sensitivity to a wide range of process inputs. Elution buffer pH was found to be the most critical process parameter, followed by resin ionic capacity. Overall, this study demonstrated the utility of the HT-IS workflow for rapid process development and characterization., (© 2024 American Institute of Chemical Engineers.)

Site-selective functionalization of the heterobenzylic C(sp 3 )-H bonds of pyridines and related heteroaromatic compounds presents challenges associated with the basic nitrogen atom and the variable reactivity among different positions on the heteroaromatic ring. Methods for functionalization of 2- and 4-alkylpyridines are increasingly available through polar pathways that leverage resonance stabilization of charge build-up at these positions. In contrast, functionalization of 3-alkylpyridines is largely inaccessible. Here, we report a photochemically promoted method for chlorination of non-resonant heterobenzylic C(sp 3 )-H sites in 3-alkylpyridines and related alkylheteroaromatics. Density functional theory calculations show that the optimal reactivity reflects a balance between the energetics of the two radical-chain propagation steps, with the preferred reagent consisting of an N -chlorosulfonamide. The operationally simple chlorination protocol enables access to heterobenzylic chlorides which serve as versatile intermediates in C-H cross-coupling reactions between heteroaromatic building blocks and diverse oxidatively sensitive nucleophiles using high-throughput experimentation., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.

High‐throughput experimentation (HTE) methods are central to modern medicinal chemistry. While many HTE approaches to C−N and Csp2−Csp2 bonds are available, options for Csp2−Csp3 bonds are limited. We report here how the adaptation of nickel‐catalyzed cross‐electrophile coupling of aryl bromides with alkyl halides to HTE is enabled by AbbVie ChemBeads technology. By using this approach, we were able to quickly map out the reactivity space at a global level with a challenging array of 3×222 micromolar reactions. The observed hit rate (56 %) is competitive with other often‐used HTE reactions and the results are scalable. A key to this level of success was the finding that bipyridine 6‐carboxamidine (BpyCam), a ligand that had not previously been shown to be optimal in any reaction, is as general as the best‐known ligands with complementary reactivity. Such "cryptic" catalysts may be common and modern HTE methods should facilitate the process of finding these catalysts. [ABSTRACT FROM AUTHOR]

In the oxidative coupling of methane (OCM), the activation of methane and the suppression of deep oxidation are in a persistent trade‐off relationship, and a catalyst design strategy that balances the activity and the selectivity is desired. In this study, we analyzed a random catalyst dataset for OCM that was earlier obtained by high‐throughput experimentation, and extracted heuristics such as elements, supports, and their combinations related to methane activation at a low temperature and selective formation of C2 compounds at a high temperature. The obtained heuristics were used for catalyst development. The most effective was the use of a mixed support between La2O3 and BaO, which improved the low‐temperature activity, the high‐temperature selectivity, as well as the maximum C2 yield. It was considered that La2O3 acted as a heater and helped low‐temperature operation of BaO, which is highly selective but not active at a low temperature. [ABSTRACT FROM AUTHOR]

A thin film combinatorial library deposited by co-sputtering of Hf, Nb and Ta was employed to characterise fundamental properties of the Hf-Nb-Ta system. Compositional mappings of microstructure and crystallography revealed similarities in alloy evolution. Distinct lattice distortion was observed upon addition of hexagonal Hf, leading to amorphisation of alloys containing more than 32 at.% Hf and less than 27 and 41 at.% Nb and Ta, respectively. Volta potential and open circuit potential mappings indicated minimal values for the highest Hf concentration. Localised anodisation of the library by scanning droplet cell microscopy revealed valve metal behaviour. Oxide formation factors above 2 nm V−1 were identified in compositional zones with high amounts of Nb and Ta. Fitting of electrochemical impedance spectroscopy data allowed electrical permittivity and resistivity of mixed oxides to be mapped. Their compositional behaviours were attributed to characteristics of the parent metal alloys and particularities of the pure oxides. Mott–Schottky analysis suggested n-type semiconductor properties for all Hf–Nb–Ta oxides studied. Donor density and flat-band potential were mapped compositionally, and their variations were found to be related mainly to the Nb amount. Synergetic effects were identified in mappings of Hf-Nb-Ta parent metals and their anodic oxides.

Most research on neurodegenerative diseases has focused on neurons, yet glia help form and maintain the synapses whose loss is so prominent in these conditions. To investigate the contributions of glia to Huntington's disease (HD), we profiled the gene expression alterations of Drosophila expressing human mutant Huntingtin (mHTT) in either glia or neurons and compared these changes to what is observed in HD human and HD mice striata. A large portion of conserved genes are concordantly dysregulated across the three species; we tested these genes in a high-throughput behavioral assay and found that downregulation of genes involved in synapse assembly mitigated pathogenesis and behavioral deficits. To our surprise, reducing dNRXN3 function in glia was sufficient to improve the phenotype of flies expressing mHTT in neurons, suggesting that mHTT's toxic effects in glia ramify throughout the brain. This supports a model in which dampening synaptic function is protective because it attenuates the excitotoxicity that characterizes HD.