"Nanozyme" is used to describe various catalysts from immobilized inorganic metal complexes, immobilized enzymes to inorganic nanoparticles. Here, the history of nanozymes is dvescribed in detail, and they can be largely separated into two types. Type 1 nanozymes refer to immobilized catalysts or enzymes on nanomaterials, which were dominant in the first decade since 2004. Type 2 nanozymes, which rely on the surface catalytic properties of inorganic nanomaterials, are the dominating type in the past decade. The definition of nanozymes is evolving, and a definition based on the same substrates and products as enzymes are able to cover most currently claimed nanozymes, although they may have different mechanisms compared to their enzyme counterparts. A broader definition can inspire application-based research to replace enzymes with nanomaterials for analytical, environmental, and biomedical applications. Comparison with enzymes also requires a clear definition of a nanozyme unit. Four ways of defining a nanozyme unit are described, with iron oxide and horseradish peroxidase activity comparison as examples in each definition. Growing work is devoted to understanding the catalytic mechanism of nanozymes, which provides a basis for further rational engineering of active sites. Finally, future perspective of the nanozyme field is discussed.

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/adma.202211041

Nanozymes: Definition, Activity, and Mechanisms.

Highly efficient gene delivery systems are essential for genetic engineering in plants. Traditional delivery methods have been widely used, such as Agrobacterium-mediated transformation, polyethylene glycol (PEG)-mediated delivery, biolistic particle bombardment, and viral transfection. However, genotype dependence and other drawbacks of these techniques limit the application of genetic engineering, particularly genome editing in many crop plants. There is a great need to develop newer gene delivery vectors or methods. Recently, nanomaterials such as mesoporous silica particles (MSNs), AuNPs, carbon nanotubes (CNTs), and layer double hydroxides (LDHs), have emerged as promising vectors for the delivery of genome engineering tools (DNA, RNA, proteins, and RNPs) to plants in a species-independent manner with high efficiency. Some exciting results have been reported, such as the successful delivery of cargo genes into plants and the generation of genome stable transgenic cotton and maize plants, which have provided some new routines for genome engineering in plants. Thus, in this review, we summarized recent progress in the utilization of nanomaterials for plant genetic transformation and discussed the advantages and limitations of different methods. Furthermore, we emphasized the advantages and potential broad applications of nanomaterials in plant genome editing, which provides guidance for future applications of nanomaterials in plant genetic engineering and crop breeding.

/pdf/the-promising-nanovectors-for-gene-delivery-in-plant-genome-3n7kqvv9.pdf

The Promising Nanovectors for Gene Delivery in Plant Genome Engineering

Plant biotic or abiotic stresses, such as pathogens, mechanical damage, or high temperature, can increase intracellular H2O2 concentration, damaging proteins, lipids, and DNA. Most current H2O2 detection methods require the separation or grinding of plant samples, inducing plant stresses, and the process is complicated and time-consuming. This paper constructed a metal-organic framework (MOF)-based biosensor for real-time, remote, and in situ detection of exogenous/endogenous H2O2 in plant organs through color-to-thermal signal conversion. By simply spraying horseradish peroxidase, 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and the precursor of zeolite imidazolate frameworks-8 (ZIF-8), ZIF-8 biosensors were formed in situ on a plant root, petiole, or leaf. This biosensor could report sub-micromolar H2O2 in plants since the oxidation products, ABTS• +, emitted heat when they absorbed energy from near-infrared (NIR) light. Due to the plant's low absorption in the NIR region, the ZIF-8 biosensor allowed for remote thermal sensing of H2O2 transport or biotic/abiotic stresses in plants with a high signal-to-noise ratio combining NIR laser and thermometer. Our biosensor can be used for the future development of plant sensors for monitoring plant signaling pathways and metabolism that are nondestructive, minimally invasive, and capable of real-time, in situ analysis.

Metal-Organic Framework-Based Biosensor for Detecting Hydrogen Peroxide in Plants through Color-to-Thermal Signal Conversion.

A review of protein hydrogels: Protein assembly mechanisms, properties, and biological applications

As a class of important artificial olfactory system, the colorimetric sensor array possesses great potential for commercialization due to its cost-effectiveness and portability. However, when applied to practical applications, the humidity interference from ambient environment and dissatisfactory sensitivity for trace target VOCs are largely unsolved problems. To overcome the problems, we developed a series of dye/MOFs@COFs gas-sensing materials with core-shell structure using a hydrophobization strategy by encapsulation of dye/metal-organic frameworks (MOFs) into hydrophobic covalent organic frameworks (COFs). Benefiting from the hydrophobic property of the COF shell, the dye/MOFs@COFs composites were endowed with excellent humidity-resistance even under 100% relative humidity (RH). Moreover, due to the uniform distribution of dyes on the porous MOFs, the dye/MOFs@COFs sensors also exhibited improved sensitivity at the sub-ppm level, compared with conventional dye sensors. On basis of the excellent humidity-resistance and improved sensitivity, an artificial olfactory array based on dye/MOFs@COFs composites was proven to be a successful practical application in early and accurate detection of wheat scab (1 day after inoculation) by monitoring its released VOC markers. The synthetic strategy for core-shell dye/MOFs@COFs is applicable to a wide range of colorimetric sensor arrays, endowing them with excellent humidity-resistance and sensitivity for the feasibility of practical applications.

Humidity-Independent Artificial Olfactory Array Enabled by Hydrophobic Core-Shell Dye/MOFs@COFs Composites for Plant Disease Diagnosis.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTNano and PlantsJillian M. Buriak*Jillian M. Buriak*Email: [email protected]More by Jillian M. Buriakhttps://orcid.org/0000-0002-9567-4328, Luis M. Liz-Marzán*Luis M. Liz-Marzán*Email: [email protected]More by Luis M. Liz-Marzánhttps://orcid.org/0000-0002-6647-1353, Wolfgang J. Parak*Wolfgang J. Parak*Email: [email protected]More by Wolfgang J. Parakhttps://orcid.org/0000-0003-1672-6650, and Xiaodong Chen*Xiaodong Chen*Email: [email protected]More by Xiaodong Chenhttps://orcid.org/0000-0002-3312-1664Cite this: ACS Nano 2022, 16, 2, 1681–1684Publication Date (Web):February 22, 2022Publication History Published online22 February 2022Published inissue 22 February 2022https://doi.org/10.1021/acsnano.2c01131Copyright © 2022 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views4947Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (3 MB) Get e-AlertsSUBJECTS:Nanomaterials,Nanoparticles,Nanotechnology,Plants,Sensors Get e-Alerts 

https://pubs.acs.org/doi/pdf/10.1021/acsnano.2c01131

Nano and Plants

A review of end-of-life crystalline silicon solar photovoltaic panel recycling technology

The mechanism of oxidizing reaction in the preparation of graphene oxide (GO) by a chemical oxidation method remains unclear. The main oxidant of graphite oxide has not been determined. Here, we show a new mechanism in which Mn2O7, the main oxidant, is heated to decompose oxygen atoms and react with graphite. The whole preparation process constitutes of four distinct independent steps, different from the three steps of literature registration, and each step has its own chemical oxidation reaction. In the first step, concentrated sulfuric acid and nitric acid are intercalated between graphite layers in the form of a molecular thermal motion to produce HNO3–H2SO4–GIC. In the second step, Mn2O7 is intercalated between graphite layers in the molecular convection–diffusion to Mn2O7–H2SO4–GIC. In the third step, Mn2O7 is decomposed by heat. Oxygen atoms are generated to oxidize the defects in the graphite layer to PGO. This discovery is the latest and most important. In the fourth step, PGO is purified with deionized water, hydrogen peroxide, and hydrochloric acid to GO. Optical microscopy, ultraviolet–visible spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction spectrometry, and scanning electron microscopy analytical evidence was used for confirming Mn2O7 as the main oxidant and the structure of GO. This work provides a more plausible explanation for the mechanism of oxidizing reaction in the preparation of GO by a chemical oxidation method.

https://pubs.acs.org/doi/pdf/10.1021/acsomega.2c01963

Mechanism of Oxidization of Graphite to Graphene Oxide by the Hummers Method

Science is communicated through language. The media of language in science is multimodal, ranging from lecturing in classrooms, to informal daily discussions among scientists, to prepared talks at conferences, and, finally, to the pinnacle of science communication, the formal peerreviewed publication. The arrival of language tools driven by artificial intelligence (AI), like ChatGPT, has generated an explosion of interest globally. ChatGPT has set the record for the fastest growing user base of any application in history, with over 100 million active users in just two months, as of the end of January 2023. ChatGPT is merely the first of many AIbased language tools, with announcements of more either in preparation or soon to be launched. Many in scientific research and universities around the world have raised concerns of ChatGPT‘s potential to transform scientific communication before we have had time to consider the ramifications of such a tool or verified that the text it generates is factually correct. The human-like quality of the text structure produced by ChatGPT can deceive readers into believing it is of human origin. It is now apparent, however, that the generated text might be fraught with errors, can be shallow and superficial, and can generate false journal references and inferences. More importantly, ChatGPT sometimes makes connections that are nonsensical and false. We have prepared a brief summary of some of the strengths and weaknesses of ChatGPT (and future AI language bots) and conclude with a set of our recommendations of best practices for scientists when using such tools at any stage of their research, particularly at the manuscript writing stage. It is important to state that even among the authors here, there is a diversity of thought and opinion, and this editorial reflects the middle ground consensus. In its current incarnation, ChatGPT is merely an efficient language bot that generates text by linguistic connections. It is, at present, “just a giant autocomplete machine”. Since ChatGPT is the first of many models that will undoubtedly improve rapidly, within a few years we will almost certainly look back at ChatGPT like an old computer from the 1980s. It must be recognized that ChatGPT relies on its existing database and content and, at the time of writing of this editorial, fails to include information published or posted after 2021, thus restricting its utility when applied to the writing of up-to-date reviews, perspectives, and introductions. Therefore, for reviews and perspectives, ChatGPT is deficient due to its lack of analytical capabilities that scientists are expected to possess and the experiences that inform us. The most important concern for us as scientists is that these AI language bots are incapable of understanding new

Best Practices for Using AI When Writing Scientific Manuscripts.

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTWhat Do We Mean When We Say Nanomedicine?Luis M. Liz-MarzánLuis M. Liz-MarzánMore by Luis M. Liz-Marzánhttps://orcid.org/0000-0002-6647-1353, Andre E. NelAndre E. NelMore by Andre E. Nelhttps://orcid.org/0000-0002-5232-4686, C. Jeffrey BrinkerC. Jeffrey BrinkerMore by C. Jeffrey Brinkerhttps://orcid.org/0000-0002-7145-9324, Warren C. W. ChanWarren C. W. ChanMore by Warren C. W. Chanhttps://orcid.org/0000-0001-5435-4785, Chunying ChenChunying ChenMore by Chunying Chenhttps://orcid.org/0000-0002-6027-0315, Xiaodong ChenXiaodong ChenMore by Xiaodong Chenhttps://orcid.org/0000-0002-3312-1664, Dean HoDean HoMore by Dean Hohttps://orcid.org/0000-0002-7337-296X, Tony HuTony HuMore by Tony Huhttps://orcid.org/0000-0002-5166-4937, Kazunori KataokaKazunori KataokaMore by Kazunori Kataokahttps://orcid.org/0000-0002-8591-413X, Nicholas A. KotovNicholas A. KotovMore by Nicholas A. Kotovhttps://orcid.org/0000-0002-6864-5804, Wolfgang J. ParakWolfgang J. ParakMore by Wolfgang J. Parakhttps://orcid.org/0000-0003-1672-6650, and Molly M. StevensMolly M. StevensMore by Molly M. Stevenshttps://orcid.org/0000-0002-7335-266XCite this: ACS Nano 2022, 16, 9, 13257–13259Publication Date (Web):September 27, 2022Publication History Published online27 September 2022Published inissue 27 September 2022https://doi.org/10.1021/acsnano.2c08675Copyright © 2022 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views4791Altmetric-Citations4LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (952 KB) Get e-AlertsSUBJECTS:Biomaterials,Materials,Nanomaterials,Nanomedicine,Nanoparticles Get e-Alerts 

Xiaodong Chen

Papers

Nano and Plants

A review of end-of-life crystalline silicon solar photovoltaic panel recycling technology

Mechanism of Oxidization of Graphite to Graphene Oxide by the Hummers Method

Best Practices for Using AI When Writing Scientific Manuscripts.

What Do We Mean When We Say Nanomedicine?