Chemical Reviews最新文献

Carbon dioxide capture, utilization, and storage (CCUS) technology is expected to be one of the key technologies for achieving a carbon-neutral society in the near future. One promising area of CCUS technology is research into artificial photosynthesis mimicking natural photosynthesis. Since natural photosynthesis uses solar energy to produce sugars such as starch and oxygen from CO₂ and water, and has long been regarded as a model for artificial photosynthesis in CO₂ utilization technology. Among various studies on artificial photosynthesis, semiartificial photosynthesis technology hybridizing biocatalysts and photocatalysts is attracting attention for CO₂ utilization. Semiartificial photosynthesis overcomes the limitations of natural photosynthesis by combining synthetic photosensitizers and biocatalysts to construct reaction systems with unique properties. To develop semiartificial photosynthesis, visible light-driven NADH regeneration, ATP regeneration, and biocatalysts for CO₂ utilization are essential components. This review provides a survey of relevant biocatalysts for visible light-driven NADH regeneration, ATP regeneration, and CO₂ utilization from the past to the present, and introduces the history of research into semiartificial photosynthesis for CO₂ or NH₃ fixation derived from these findings.

Bioorthogonal chemistry was introduced in the context of the molecular turn-on optical imaging and therapy over 20 years. A pivotal innovation in this field is the development of bioorthogonal turn-on optical probes (BioTOPs), which integrate bioorthogonal handles with imaging agents to leverage selective bioorthogonal reactions for activating optical signals, enabling high-contrast and real-time visualization of biomolecules in living systems. The precise spatiotemporal control over signal activation also enables minimal off-target therapy. This review summarizes recent advances in the design and application of BioTOPs. We first discuss the two-step pretargeted strategy of bioorthogonal turn-on optical imaging. Then, we categorize the bioorthogonal reactions that have been employed for signal activation, including Staudinger ligations, strain-promoted cycloadditions, metal-catalyzed reactions, ketone/aldehyde condensations, boron compound-enabled reactions, and thiol-selective bioconjugations, alongside the activation mechanisms, such as click-to-release, energy/electron dequenching, steric hindrance dequenching, bioorthogonal handle decaging, fluorogenic handle conjugation, and fluorogenic self-assembly, which collectively offer tunable kinetics and high signal turn-on folds. We conclude with applications in diagnostic and therapeutic contexts, from biomolecule profiling and mapping to cancer diagnosis and disease-specific interventions while offering a perspective on the future transformative potential of this technology for molecular diagnostics.

The ocean, Earth’s largest carbon reservoir, exerts a central role over atmospheric CO₂ through its capacity to store carbon primarily as bicarbonate ions. Direct observations indicate that the global ocean has a net carbon uptake of 2.6–3.0 petagrams of carbon annually, representing nearly 30% of anthropogenic CO₂ emissions. This review examines two principal domains of oceanic carbon cycling. The first concerns the natural uptake and storage of anthropogenic CO₂, with emphasis on the response of the marine carbonate system and the spatial distribution of absorbed carbon. The second addresses emerging marine CO₂ removal strategies, especially ocean alkalinity enhancement and macroalgae-based approaches. Ocean alkalinity enhancement aims to increase seawater buffering capacity to facilitate greater CO₂ uptake, whereas macroalgae-based strategies rely on photosynthetic fixation and the subsequent storage of organic and inorganic carbon in various reservoirs. Effective implementation of these approaches necessitates rigorous monitoring, reporting, and verification frameworks to ensure their quantifiable efficacy and environmental integrity.

The performance of rechargeable batteries is fundamentally influenced by the physicochemical properties and microstructural features of their key material components. Recent experimental advancements have highlighted the potential of single-crystal (SC) morphologies to address inherent limitations of polycrystalline (PC) electrodes and solid-state electrolytes, offering tunable charge transport kinetics and improved cell cycling performance. This review examines how state-of-the-art computational modeling, from atomistic and mesoscale to continuum-level approaches, including machine learning methodologies, has been utilized to investigate the critical factors governing the electrochemical behavior of SC battery materials. We explore how predictive modeling can elucidate the processing–structure–property–performance relationships of SC cathodes, anodes, and solid-state electrolytes, with a focus on unique SC characteristics such as crystallographic anisotropy, size effects, and facet-dependent properties. Additionally, we identify limitations in commonly used modeling techniques and discuss strategies to address these challenges. By integrating high-fidelity simulations with experimental insights, this review aims to outline a clear path for the rational design and optimization of SC battery components, paving the way for accelerated advancements in energy storage technologies.

One of the primary challenges in quantum chemistry is the accurate modeling of strong electron correlation. While multireference methods effectively capture such correlation, their steep scaling with system size prohibits their application to large molecules and extended materials. Quantum embedding offers a promising solution by partitioning complex systems into manageable subsystems. In this Review, we highlight recent advances in multireference density matrix embedding and localized active space self-consistent field approaches for complex molecules and extended materials. We discuss both classical implementations and the emerging potential of these methods on quantum computers. By extending classical embedding concepts to the quantum landscape, these algorithms have the potential to expand the reach of multireference methods in quantum chemistry and materials.

Industrial chemicals are characterized by their substantial production volumes, widespread applications, fugitive release into the environment, and the general lack of full awareness regarding their risks, carrying global unintended adverse effects on human and ecological health. In the ongoing pursuit of more sustainable and less hazardous industrial chemicals, a tremendous body of research has been developed. However, reliance on empirical molecular design based solely on human knowledge and expertise may not be adequate for avoiding regrettable substitution. Recent advances in generative machine learning (ML) technologies, and their applications in ML-assisted molecular design, possess immense promise to bring innovative solutions for green substitution of hazardous industrial chemicals. This review outlines the methodologies of ML-assisted molecular design and proposes design strategies for green alternative chemicals that possess both necessary functionalities and low environmental hazards throughout their life cycles. Additionally, case examples are provided to illustrate the methodologies and highlight areas that warrant further research, including the development of AI agents for both chemical risk management and green substitution. Applications of the methodologies can yield a sustainable and responsible way that both promotes the benefits of industrial chemicals and simultaneously minimizes their adverse impacts on humans and the environment.

Helices are ubiquitous in Nature and play indispensable roles in biological systems. A helix with an excess of one-handed helicity can be optically active because left- and right-handed helices are nonsuperimposable enantiomers. Stimulated by natural helices and their broad applications as chiral materials, artificial helical polymers have long been a hot research topic. Here, we describe recent advances in the controlled synthesis, structures, and functions of artificial helical polymers over the past decades. The main topics of this review include the controlled synthesis of one-handed static helical polymers through asymmetric polymerization and helix-sense-selective polymerization strategies and the precise fabrication of one-handed preferred dynamic helices via helix induction and memory. The remarkable progress in the applications of helical polymers in enantiomer separation, asymmetric catalysis, chiral self-assembly, and circularly polarized luminescence is then systematically summarized. Finally, the remaining challenges and future perspectives in the research areas of artificial helical polymers and related chiral materials are discussed.

Polaritonic chemistry has garnered increasing attention in recent years due to pioneering experimental results, which show that site- and bond-selective chemistry at room temperature is achievable through strong collective coupling to field fluctuations in optical cavities. Despite these notable experimental strides, the underlying theoretical mechanisms remain unclear. In this focus review, we highlight a fundamental theoretical link between the seemingly unrelated fields of polaritonic chemistry and spin glasses, exploring its profound implications for the theoretical framework of polaritonic chemistry. Specifically, we present a mapping of the dressed many-molecules electronic-structure problem under collective vibrational strong coupling to the analytically solvable spherical Sherrington-Kirkpatrick (SSK) model of a spin glass. This mapping uncovers a collectively induced spin glass phase of the intermolecular electron correlations, which could provide the long sought-after seed for significant local chemical modifications in polaritonic chemistry. Overall, the qualitative predictions made from the SSK solution (e.g., dispersion effects, phase transitions, differently modified bulk and rare event properties, heating, etc.) agree well with available experimental observations. Our connection not only demonstrates the relevance of moving beyond the dilute gas approximation, where the Fermionic nature of the electrons becomes an essential ingredient, but it also paves the way for novel computational strategies to quantify the subtle chemical characteristics of the cavity-induced spin glass phase. Moreover, our mapping provides a versatile framework to incorporate, adapt, and explore a wide range of spin glass concepts within polaritonic chemistry. Ultimately, the connection also offers fresh insights into the applicability of spin glass theory beyond condensed matter systems suggesting novel theoretical directions such as spin glasses with explicitly time-dependent (random) interactions.

Templating methods have emerged as a powerful toolbox for the rational design and scalable fabrication of nanostructured and hierarchical materials with controlled morphology, dimensionality, and spatial organization. By leveraging predefined scaffolds across molecular, colloidal, and macroscopic length scales, templated synthesis and template-assisted self-assembly enable the bottom-up construction of materials with tailored structural and functional properties. This review provides a comprehensive overview of templating strategies categorized by operational scale and templating modality. We first discuss nanoscale and microscale templating approaches based on colloidal, molecular, and other noncolloidal templates. We then examine template-assisted self-assembly strategies guided by nanoscale and macroscale templates that facilitate the organization of building blocks into ordered architectures. Next, multiscale integration strategies that bridge bottom-up and top-down fabrication are reviewed, including physical-field-directed assembly and 3D-printed templates. Finally, representative applications in photonics, energy conversion and storage, and biomedicine are presented, followed by an outlook on future opportunities and challenges in the fabrication of hierarchical materials via templating.

Polyesters represent a versatile class of materials whose biodegradability, biocompatibility, mechanical tunability, and broad chemical design space have made them valuable across a wide range of application areas, including tissue engineering, biomedical engineering, sustainable manufacturing, and soft robotics. Light-based 3D printing has further expanded their potential by enabling precise spatial control across nano- to macroscales, supporting the fabrication of resorbable implants, drug-delivery systems, microneedle arrays, and stimuli-responsive materials. This review discusses the essential steps toward light-based 3D printing of polyesters from synthetic strategies for producing these materials to functionalization methods that render them suitable for light-based 3D printing. Particular attention is given to the synthetic origin of the polyester, the way photoreactive groups are introduced and organized within the network, and how the formulation of the resulting photoresin together govern the ultimate photoreactivity, degradation behavior, print resolution, and mechanical performance. Advantages and limitations of current photochemical approaches are discussed across different light-based 3D printing technologies. With continuing advancements in manufacturing, the field of light-based 3D printing of polyesters shows substantial promise, poised to redefine material design, and influence a broad range of future technologies.