Technological Innovations in Photochemistry for Organic Synthesis: Flow Chemistry, High-Throughput Experimentation, Scale-up, and Photoelectrochemistry

Chemical Reviews, Journal Year: 2021, Volume and Issue: 122(2), P. 2752 - 2906

Published: Aug. 10, 2021

Photoinduced chemical transformations have received in recent years a tremendous amount of attention, providing plethora opportunities to synthetic organic chemists. However, performing photochemical transformation can be quite challenge because various issues related the delivery photons. These challenges barred widespread adoption steps industry. past decade, several technological innovations led more reproducible, selective, and scalable photoinduced reactions. Herein, we provide comprehensive overview these exciting advances, including flow chemistry, high-throughput experimentation, reactor design scale-up, combination photo- electro-chemistry.

Language: Английский

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A field guide to flow chemistry for synthetic organic chemists

Chemical Science, Journal Year: 2023, Volume and Issue: 14(16), P. 4230 - 4247

Published: Jan. 1, 2023

This review explores the benefits of flow chemistry and dispels notion that it is a mysterious “black box”, demonstrating how can push boundaries organic synthesis through understanding its governing principles.

Language: Английский

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Bayesian Optimization of Computer-Proposed Multistep Synthetic Routes on an Automated Robotic Flow Platform

ACS Central Science, Journal Year: 2022, Volume and Issue: 8(6), P. 825 - 836

Published: June 10, 2022

Computer-aided synthesis planning (CASP) tools can propose retrosynthetic pathways and forward reaction conditions for the of organic compounds, but limited availability context-specific data currently necessitates experimental development to fully specify process details. We plan optimize a CASP-proposed human-refined multistep route toward an exemplary small molecule, sonidegib, on modular, robotic flow platform with integrated analytical technology (PAT) data-rich experimentation. Human insights address catalyst deactivation improve yield by strategic choices order addition. Multi-objective Bayesian optimization identifies optimal values categorical continuous variables in involving 3 reactions (including heterogeneous hydrogenation) 1 separation. The platform's modularity, reconfigurability, flexibility convergent are shown be essential allowing variation downstream residence time processes controlling addition minimize undesired reactivity. Overall, work demonstrates how automation, machine learning, robotics enhance manual experimentation through assistance idea generation, design, execution, optimization.

Language: Английский

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Automated self-optimization, intensification, and scale-up of photocatalysis in flow

Science, Journal Year: 2024, Volume and Issue: 383(6681)

Published: Jan. 25, 2024

The optimization, intensification, and scale-up of photochemical processes constitute a particular challenge in manufacturing environment geared primarily toward thermal chemistry. In this work, we present versatile flow-based robotic platform to address these challenges through the integration readily available hardware custom software. Our open-source combines liquid handler, syringe pumps, tunable continuous-flow photoreactor, inexpensive Internet Things devices, an in-line benchtop nuclear magnetic resonance spectrometer enable automated, data-rich optimization with closed-loop Bayesian strategy. A user-friendly graphical interface allows chemists without programming or machine learning expertise easily monitor, analyze, improve photocatalytic reactions respect both continuous discrete variables. system's effectiveness was demonstrated by increasing overall reaction yields improving space-time compared those previously reported processes.

Language: Английский

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In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(40), P. 21699 - 21716

Published: Sept. 27, 2023

Exceptional molecules and materials with one or more extraordinary properties are both technologically valuable fundamentally interesting, because they often involve new physical phenomena compositions that defy expectations. Historically, exceptionality has been achieved through serendipity, but recently, machine learning (ML) automated experimentation have widely proposed to accelerate target identification synthesis planning. In this Perspective, we argue the data-driven methods commonly used today well-suited for optimization not realization of exceptional molecules. Finding such outliers should be possible using ML, only by shifting away from traditional ML approaches tweak composition, crystal structure, reaction pathway. We highlight case studies high-Tc oxide superconductors superhard demonstrate challenges ML-guided discovery discuss limitations automation task. then provide six recommendations development capable discovery: (i) Avoid tyranny middle focus on extrema; (ii) When data limited, qualitative predictions direction than interpolative accuracy; (iii) Sample what can made how make it defer optimization; (iv) Create room (and look) unexpected while pursuing your goal; (v) Try fill-in-the-blanks input output space; (vi) Do confuse human understanding model interpretability. conclude a description these integrated into workflows, which enable materials.

Language: Английский

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Progress in ATRP-derived materials for biomedical applications

Progress in Materials Science, Journal Year: 2024, Volume and Issue: 143, P. 101248 - 101248

Published: Feb. 11, 2024

Language: Английский

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Two-Dimensional Transition Metal Dichalcogenides: A Theory and Simulation Perspective

Chemical Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Two-dimensional transition metal dichalcogenides (2D TMDs) are a promising class of functional materials for fundamental physics explorations and applications in next-generation electronics, catalysis, quantum technologies, energy-related fields. Theory simulations have played pivotal role recent advancements, from understanding physical properties discovering new to elucidating synthesis processes designing novel devices. The key has been developments ab initio theory, deep learning, molecular dynamics, high-throughput computations, multiscale methods. This review focuses on how theory contributed progress 2D TMDs research, particularly twisted moiré-based TMDs, predicting exotic phases TMD monolayers heterostructures, nucleation growth synthesis, comprehending electron transport characteristics different contacts potential devices based heterostructures. notable achievements provided by highlighted, along with the challenges that need be addressed. Although demonstrated prototype created, we conclude highlighting research areas demand most attention simulation might address them aid attaining true toward commercial device realizations.

Language: Английский

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Towards the Standardization of Flow Chemistry Protocols for Organic Reactions

Chemistry - Methods, Journal Year: 2021, Volume and Issue: 1(11), P. 454 - 467

Published: Sept. 13, 2021

Abstract Flow chemistry studies can sometimes be difficult to reproduce. In this article we provide guidance scientists for experimental details that should considered as part of any organic chemistry‐based continuous flow study. A focus is placed on information provided within reported enable experiments more easily and reliably reproduced. Topics covered include reactor components assembly, important parameter effects useful performance criteria. The covers aspects homogeneous systems, multiphase transformations, catalytic reactions (homogeneous heterogeneous). detailed discussion photochemistry, biocatalysis electrochemical systems outside the scope review.

Language: Английский

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From Platform to Knowledge Graph: Evolution of Laboratory Automation

JACS Au, Journal Year: 2022, Volume and Issue: 2(2), P. 292 - 309

Published: Jan. 10, 2022

High-fidelity computer-aided experimentation is becoming more accessible with the development of computing power and artificial intelligence tools. The advancement experimental hardware also empowers researchers to reach a level accuracy that was not possible in past. Marching toward next generation self-driving laboratories, orchestration both resources lies at focal point autonomous discovery chemical science. To achieve such goal, algorithmically data representations standardized communication protocols are indispensable. In this perspective, we recategorize recently introduced approach based on Materials Acceleration Platforms into five functional components discuss recent case studies focus representation exchange scheme between different components. Emerging technologies for interoperable multi-agent systems discussed their applications automation. We hypothesize knowledge graph technology, orchestrating semantic web systems, will be driving force bring knowledge, evolving our way automating laboratory.

Language: Английский

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Autonomous Multi‐Step and Multi‐Objective Optimization Facilitated by Real‐Time Process Analytics

Advanced Science, Journal Year: 2022, Volume and Issue: 9(10)

Published: Feb. 1, 2022

Autonomous flow reactors are becoming increasingly utilized in the synthesis of organic compounds, yet complexity chemical reactions and analytical methods remains limited. The development a modular platform which uses rapid NMR FTIR measurements, combined with chemometric modeling, is presented for efficient timely analysis reaction outcomes. This tested four variable single-step (nucleophilic aromatic substitution), to determine most effective optimization methodology. self-optimization approach minimal background knowledge proves provide optimal parameters within shortest operational time. chosen then applied seven two-step problem (imine formation cyclization), active pharmaceutical ingredient edaravone. Despite exponentially increased this problem, achieves excellent results relatively small number iterations, leading >95% solution yield intermediate up 5.42 kg L

Language: Английский

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