Tag: multidisciplinary

  • Nanoscale imaging and control of altermagnetism in MnTe

    Nanoscale imaging and control of altermagnetism in MnTe

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    Our vector mapping includes the local real-space detection of the orientation of the altermagnetic order vector, L = M1 − M2, with respect to the MnTe crystal axes in the (0001)-plane by X-ray magnetic linear dichroism (XMLD)-PEEM, and of the sign of L for a given crystal orientation by including X-ray magnetic circular dichroism (XMCD)-PEEM. In antiferromagnets with opposite spin sublattices connected by translation or inversion, the \({\mathcal{T}}\)-odd XMCD is excluded by symmetry. In such cases, only the L axis can be detected by the \({\mathcal{T}}\)-even XMLD-PEEM, but the sign of L remains unresolved25,26,27,28,29,30. Contrary to this, the recent theoretical and experimental spectroscopic study of altermagnetic MnTe has demonstrated the presence of a sizable XMCD, reflecting the \({\mathcal{T}}\)-symmetry breaking in the electronic structure by the altermagnetic g-wave spin polarization12. Furthermore, the XMCD spectral shape owing to L pointing in the (0001) plane is qualitatively distinct from the XMCD spectral shape owing to a net magnetization M = M1 + M2 along the [0001] axis12. This was demonstrated in ref. 12 by comparing the measured XMCD spectral shapes at a zero magnetic field and at a 6-T field applied along the [0001] axis. In the former case, M is weak and the measured spectral shape agrees with the predicted spectral shape due to L. In the latter case, M is sizable and qualitatively modifies the spectral shape, again in agreement with theory. We performed normal incidence X-ray PEEM, which is the optimum geometry for measuring both the in-plane Néel axis in the XMLD, and the altermagnetic XMCD. Images are taken at zero external field, where the XMCD signal owing to the weak relativistic remnant M is negligible compared with the altermagnetic XMCD owing to \({\bf{L}}\parallel \langle 1\bar{1}00\rangle \) directions in the (0001) plane12. The latter gives rise to our measured XMCD-PEEM contrast as confirmed by its spectral dependence (Methods and Extended Data Fig. 1). In analogy to the d.c. anomalous Hall effect, the XMCD can be described by the Hall vector, \({\bf{h}}=({\sigma }_{zy}^{a},{\sigma }_{xz}^{a},{\sigma }_{yx}^{a})\), where σij = −σji are the antisymmetric components of the frequency-dependent conductivity tensor. For L in the (0001) plane of MnTe, h points along the [0001] axis, that is, \({\sigma }_{zy}^{a}={\sigma }_{xz}^{a}=0\) and \({\sigma }_{yx}^{a}\ne 0\), with the exception of \({\bf{L}}\parallel \langle 2\bar{1}\bar{1}0\rangle \) axes where \({\sigma }_{yx}^{a}=0\) by symmetry.

    The method of combining the XMCD-PEEM and XMLD-PEEM images into the vector map of L is illustrated in Fig. 1b. As the L vector subtends the angle, ϕ, in the MnTe (0001) plane relative to the \([1\bar{1}00]\) axis, the XMCD is proportional to cos(3ϕ), with maximum magnitude for \({\bf{L}}\parallel \langle 1\bar{1}00\rangle \) -axes and vanishing for \({\bf{L}}\parallel \langle 2\bar{1}\bar{1}0\rangle \) axes12. An XMCD-PEEM image of a 25μm2 unpatterned area of MnTe is shown in Fig. 1c, where positive and negative XMCD appear as light and dark contrast, respectively. The corresponding three-colour XMLD-PEEM map, shown in Fig. 1d, was obtained from a set of PEEM images taken with the X-ray linear polarization rotated, within the MnTe (0001) plane, in 10° steps from −90° to +90° relative to the horizontal [\(1\bar{1}00\)] axis. In this image, the local L-vector axis is distinguished (by red–green–blue colours), but the absolute direction remains unresolved. This information is included by combining the XMCD-PEEM and XMLD-PEEM in a six-colour vector map, shown in Fig. 1e,f, where positive XMCD regions change the colour (red–green–blue to orange–yellow–purple) of the XMLD-PEEM map and negative XMCD regions leave it unchanged. The Mn L2,3 X-ray absorption and altermagnetic XMCD spectra are shown in Fig. 1g. The XMCD-PEEM images are obtained at fixed energy corresponding to the peak in the altermagnetic XMCD at the L2 edge. The XMCD contrast reverses between positive and negative peaks of the XMCD spectrum, as shown in Extended Data Fig. 1, and vanishes at elevated temperatures where the spontaneous anomalous Hall effect is absent, as shown in Extended Data Fig. 2.

    The characteristic vector mapping of L in our unpatterned MnTe film, shown in Fig. 1e,f, shows a rich landscape of (meta)stable textures akin to earlier reports in compensated magnets26,27,28,29,30. There exist 60° and 120° domain walls separating domains with L aligned along the different easy axes, as well as vortex-like textures. Highlighted in Fig. 1f is an example of an altermagnetic vortex–antivortex pair, analogous to magnetic textures previously detected in antiferromagnets such as CuMnAs (ref. 30). However, only the XMLD-PEEM was available in the antiferromagnet30, that is, only the spatially varying Néel-vector axis could be identified, similar to our XMLD-PEEM image in Fig. 1d. In our altermagnetic case, we can add the information from the measured XMCD-PEEM (Fig. 1c). This allows us to plot the vector map of L, as shown in Fig. 1e,f. We directly experimentally determine that the L vector makes a clockwise rotation by 360° around the first vortex nanotexture, indicated by the magenta–white circle, whereas the other nanotexture is an antivortex with an opposite winding of the L vector, indicated by the cyan–white circle.

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  • Will artificial intelligence help or hinder progress on the SDGs?

    Will artificial intelligence help or hinder progress on the SDGs?

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    Dr Serge Stinckwich pictured speaking into a microphone.

    Serge Stinckwich is working to understand how to use AI tools sustainably.Credit: UNDESA/DPIDG/UNPOG

    There is a lot of interest from inside the United Nations around how artificial intelligence (AI) can be used to speed up progress towards its 17 Sustainable Development Goals (SDGs), says computer scientist Serge Stinckwich.

    As head of research at the United Nations University Institute in Macau (UNU Macau), which was established by the UN in 1992 to do research and training on the use of digital technologies in addressing global issues, Stinckwich is interested in how AI can help countries to hit their SDG targets by the 2030 deadline.

    Any gains made using AI will come with costs, however. A notoriously power-hungry resource that is vulnerable to bias and inequitable access, AI presents its own challenges.

    Stinckwich spoke to Nature Index about how institutions can use AI tools responsibly to power their SDG-related research.

    What is one example of how AI can be used to speed up progress towards the SDGs?

    The popularity of large language models (LLMs) has caused a rapid escalation in the amount of data being used to train AI systems. There’s now a scarcity of machine-readable, diverse data on the Internet for training AI algorithms. Synthetic data, which are generated using algorithms and simulations that mimic real-world scenarios, provide a way to train AI models on more data than would usually be possible.

    Synthetic data can help to rebalance biased data sets — for example, in a data set skewed towards one gender, synthetic data can be added to balance representation. They can also help to address the problem of scarcity or missing data. This can be particularly useful in medical research, in which people’s health data and personal information can be hard to obtain because of privacy issues.

    This approach will become increasingly common. Gartner, a research and consulting firm headquartered in Stamford, Connecticut, predicts that by the end this year, more than 60% of the data used to train machine learning models will be synthetic.

    What are the risks in using synthetic data?

    Synthetic data are generated from data sets that already exist. So, biases in the initial data sets could be propagated throughout the synthetic data, and in turn, AI models that have been trained on them. Our work at UNU Macau focuses on understanding the impact of synthetic data used in machine learning, including the risks for sustainable development through research.

    Last year, for instance, we published a technology brief in which we tried to identify the benefits and risks of using synthetic data in AI training. On the basis of this work, we proposed guidelines for responsible use of synthetic data in research related to SDGs, especially in poorer countries. This includes using diverse data when creating synthetic data sets, which means including a wide range of demographics, environments and conditions. We also recommend disclosing or watermarking all synthetic data and their sources, disclosing quality metrics for synthetic data and prioritizing the use of non-synthetic data when possible.

    We also recommend that institutions and organizations establish global quality standards, security measures and ethical guidelines for generation and use of synthetic data.

    We hope that UN member states and agencies will adopt our guidelines to support policy-making in the global governance of AI.

    What other AI tools or resources are making a difference in SDG-related research?

    When I was a researcher at the French Research Institute on Sustainable Development (IRD) in Marseille, I worked on a project called Deep2PDE in Cameroon. Together with colleagues at the local universities, our team used machine-learning tools to understand how competition for light between plant species affects agroforests in which cocoa trees are grown alongside other trees and crops. This helped us to simulate, design and test systems to optimize cocoa production.

    There are lots of practical applications of AI, such as this one, that can aid progress towards the SDGs. A big advantage is that these tools can help teams to tailor their work to the needs and contexts of communities; what might be useful for people in Europe or North America might not work in Africa.

    What are the other risks of using AI more generally to progress SDG research?

    We need big computing infrastructure to power AI systems, and this requires resources such as water for cooling systems. This has implications for sustainability, and by extension, the SDGs. So, we have to be cautious. The environmental impacts of AI systems, including on the use of minerals and water and greenhouse-gas emissions, is a big concern. For instance, some research suggests that training an LLM, such as the one powering the chatbot ChatGPT, could produce carbon emissions equivalent to those from roughly 500–600 flights between New York City and Los Angeles, California.

    Some technology companies are not keen to share the actual cost of their AI systems and the resources they use. This makes it difficult for researchers to evaluate the environmental impacts of AI and to advise governments and policymakers on how to mitigate them.

    Another major issue is one of inequity: AI tools and data are often owned and controlled by companies and institutions in richer countries, so poorer countries are limited in how they can use them to further their SDG-related research.

    How can the equity problem be addressed?

    A big reason for this issue is that most of the progress in building LLMs in the past few years has been done by private companies, not by academics and research institutions. Some potential solutions include creating public–private partnerships and initiatives to democratize access to computing infrastructure.

    For example, the Swiss International Computation and AI Network, run by the Swiss Federal Institute of Technology in Zurich, aims to give researchers from low-income settings access to supercomputing resources so that they can develop AI tools that benefit the world. They’re partnering with organizations such as Data Science Africa, a non-profit group in Nairobi, to empower young Africans to use data science to develop solutions for local problems and to help reduce inequalities in data and software infrastructures.

    Some online platforms, such as the one run by Hugging Face, a technology company in New York City, make AI-tool-building infrastructure accessible to everyone. It’s open-source, allowing users to share and access resources, including data sets and models developed by others. This approach can help to reduce resource consumption and the environmental impact of AI development.

    This interview has been edited for length and clarity.

    Nature Index’s news and supplement content is editorially independent of its publisher, Springer Nature. For more information about Nature Index, see the homepage.

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  • Liquid metal pumps itself out of gels to make artificial vasculature

    Liquid metal pumps itself out of gels to make artificial vasculature

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  • Structure of apolipoprotein B100 bound to the low-density lipoprotein receptor

    Structure of apolipoprotein B100 bound to the low-density lipoprotein receptor

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    Sacrificial capillary pumps to engineer multiscalar biological forms

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  • Five countries having a clear impact on the latest materials-science research

    Five countries having a clear impact on the latest materials-science research

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    Materials science is a field that is seeing strong growth in the Nature Index. From 2019 to 2023, there was a 25% increase in the number of articles in the database related to the subject and last year these papers represented 27.2% of all research in its journals. In terms of the countries publishing this research, China now represents around half of global output. But other Asian nations, and some outside the region too, are making significant contributions. Here, we focus on five countries whose materials-science output over the period is noticeable for variety of reasons. South Korea has consistently maintained a top-five position by growing its Share, India is a rapidly emerging force in the field, Singapore stands out as a key location despite its size, and two European nations — Italy and Denmark — seem to be bucking the trend for Western countries losing Share.

    South Korea

    Population (2023): 51.71 million

    GDP per capita (2022): US$32,395

    Researchers (full-time equivalent) per million inhabitants (2021): 9,081.9

    Nature Index materials science rank: 5

    South Korea is an important player in materials science and has ranked fifth by Share in the field every year from 2019 to 2023. The field is a clear priority for the country, as reflected by its proportion of materials-science Share relative to its overall Nature Index output (49.7%), a figure that is higher than China (45.7%) and more than three times higher than that of the United States (15.4%).

    The quality of its materials-science output is shown by South Korea-based researchers authoring the most highly cited Nature Index paper in the field in 2023. The scientists, mainly from the Ulsan National Institute of Science and Technology, developed a perovskite solar cell with a certified power-conversion efficiency of 25.7%. The article1, which was published in Nature, had been cited 972 times at the time of Nature Index’s data analysis.

    The country is a big spender on research and development (R&D) and has world-leading numbers of researchers relative to its population size, according to statistics from the United Nations cultural organization UNESCO. But with a birthrate that is also the lowest in the world and dwindling numbers of students going into higher education, South Korea has challenges to overcome to remain as a global leader in science.

    India

    Population (2023): 1.43 billion

    GDP per capita (2022): US$2,366

    Researchers (full-time equivalent) per million inhabitants (2020): 260.4

    Nature Index materials science rank: 7

    India’s ranking in materials science in the Nature Index has been steadily rising, climbing six places from 2019 to 2023 with its Share rising by 70% across this time period, from 234.11 in 2019 to 397.64 in 2023. Between 2022 and 2023, India’s percentage increase in Share was 33%, higher than China’s (22%), although these figures represent increases in Share of 97.85 for India and 1,879.29 for China.

    India also has the lowest article Count among the ten leading countries in materials science. This indicates that scientists from India are, on average, collaborating less with researchers in other countries on materials-science papers tracked by the Nature Index, which could be an area to improve in the coming years. India did not feature in any of the ten leading country pairs in materials science by bilateral collaboration score (CS), in contrast to China which features in seven of these.

    Given its large population and commitment to materials science as a priority area (its materials-science Share relative to overall Share is 26.6%), India has huge potential to further climb the rankings. But it might need to ensure it invests in retaining domestic talent before it moves to other countries. Although the Indian government has attracted science and technology students using large numbers of scholarships and fellowships, the country’s research expenditure (from government and non-government sources) as a percentage of GDP (0.64% in 2020–21) is much lower than major research systems such as China (2.56% in 2022) and the United States (3.59% in 2022). One way to boost this figure will be increased private investment, which at around 40% of total research spending is a relatively low proportion by international standards.

    Singapore

    Population (2023): 5.92 million

    GDP per capita (2022): US$88,429

    Researchers (full-time equivalent) per million inhabitants (2021): 7,425.8

    Nature Index materials science rank: 10

    Singapore is a country that has a very high concentration on materials science: among the leading 20 nations, it has the second-highest percentage for materials-science output as a proportion of overall output in Nature Index journals (48.6%). Although the city state is the smallest country in southeast Asia by surface area, its research collaboration with China in the subject is the second strongest in the world with a bilateral collaboration score (CS) of 400.2; effectively meaning the partnership produced 400 Nature Index papers together. Singapore’s contribution to this collaboration was also far from small: it represented around a third of the CS produced by the pair. The leading international institutional partnership in materials science involving Singapore in 2023 was between the National University of Singapore (NUS) and Tianjin University, China, with a CS of 43.04. The NUS was also the third highest non-Chinese academic institution for materials science in the world by Share, after the Massachusetts Institute of Technology and University of Tokyo

    The importance of the Singapore–China collaboration might reflect the city state’s ongoing engagement with China’s global infrastructure development strategy, the Belt and Road Initiative (BRI). A previous analysis by Nature Index showed Singapore as China’s strongest BRI partner. But country-specific trends in how researchers are identifying themselves on papers might mean high international collaboration scores between China and countries such as Singapore might in part be made up by Chinese researchers working with Chinese researchers.

    Italy

    Population (2023): 58.76 million

    GDP per capita (2022): US$35,069

    Researchers (full-time equivalent) per million inhabitants (2021): 2,677.8

    Nature Index materials science rank: 14

    Italy performs consistently in the rankings for materials science, placing 14th in the past three years, an improvement on one place from 2019 and 2020. Its Share of 217.13 was lower than Singapore (263.89) and South Korea (810.22) but it was the top-ranked European country for change in Share from 2019 to 2023, with a 13.5% increase across the period

    Its relatively strong performance in the field might reflect a prioritization of physical science more generally: it was ranked eighth globally for the subject with a Share of 634.19 in the Nature Index 2024 Research Leaders tables, ahead of Switzerland, India and Canada. Across all subjects, however, Italy ranks 11th in the world — below these three countries — with a Share of 1313.44.

    According to data from a 2021 survey funded by the European Commission — the Mobility Patterns and Career Paths of EU Researchers — researchers and PhD students from Italy move between countries significantly more than the European Union average, to seek better working conditions and pay, and advance their careers abroad. Talent retention, therefore, is something Italy might need to consider if it is looking to become one of the leading countries in materials science.

    Denmark

    Population (2023): 5.95 million

    GDP per capita (2022): US$67,790

    Researchers (full-time equivalent) per million inhabitants (2021): 7,707.7

    Nature Index materials science rank: 22

    Denmark has seen a steady increase in its Share in materials science across the five years of Nature Index’s latest analysis (2019 to 2023) with a rise of 15% across the period. But in terms of ranking, it dropped two places from 2022 to 2023 and might need to do more to break into the leading 20 countries given its relatively small output. There are also much bigger countries seeing large increases in Share: across the same period India saw its output increase by 70%, whereas China’s went up 85%.

    There is evidence that Denmark punches above its weight in the field, however: normalizing its Share for population gives it a higher figure per million people than the United States, United Kingdom or Germany. Denmark is a country keen to be a leader of the green transition and new technologies associated with this, often using its natural strengths such as connections to sea-related industries. In 2023, researchers from Aarhus University showed that chemical recycling approaches for thermoset epoxy resins and composites were achievable2. These materials are found in the shells of wind-turbine blades and this new approach could lead to a reduction in the number of wind-turbine blades sent to landfill.

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  • A stable zeolite with atomically ordered and interconnected mesopore channel

    A stable zeolite with atomically ordered and interconnected mesopore channel

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    OSDA synthesis

    The OSDA used in this work is a di-quaternary phosphonium cation (bis-1,8(tricyclohexyl phosphonium) octamethylene, denoted as Tri-Cy-dC8, consisting of tricyclohexylphosphonium head groups connected by a linear methylene chain with eight carbons (Extended Data Fig. 1a). The synthesis of Tri-Cy-dC8 was carried out by the reaction of tricyclohexylphosphine with corresponding linear dibromoalkanes, Br(CH2)8Br. In a typical synthesis, 36.514 g (0.125 mol) of tricyclohexylphosphine (Aladin, >96%) was dissolved in 150 ml chloroform (Sinopharm, ≥99%) in a three-neck 500-ml flask immersed in an ice bath. The linear 1,8-dibromooctane (0.05 mol, Aladin, 98%) dissolved in 50 ml chloroform was added dropwise into the flask through an addition funnel. The mixture was stirred for 2 h and then heated to reflux for 4 days. After cooling to ambient temperature, excessive ethyl acetate was added to precipitate the white product, which was washed twice with ether and subjected to rotary evaporation to obtain the pure white OSDA dibromide salts. The structure and purity of the products were confirmed by liquid 1H and 13C NMR in D2O (Supplementary Figs. 1 and 2).

    The dibromide salts were converted to their corresponding hydroxide forms by anion exchange using anion exchange resin (Xidian, 1.1 mequiv/1 ml) in batch mode. The hydroxide solution was concentrated by rotary evaporation under vacuum. The concentration of the final solution was determined by titration with 0.1 N HCl (Beijing North Weiye Institute of Measuring and Testing Technology) using phenolphthalein as an indicator, and the weight percentage of the OSDA(OH)2 is generally about 30 wt%.

    Zeolite syntheses

    Synthesis in the hydroxide medium

    ZMQ-1 zeolite was initially discovered in hydroxide-mediated synthesis at 180 °C with a gel molar composition of 1.0SiO2:0.02Al2O3:0.25Tri-Cy-dC8(OH)2:10H2O. A typical synthesis procedure was as follows. 0.458 g (0.0022 mol) of aluminium isopropoxide (Macklin, AR) was dissolved in the OSDA(OH)2 solution (0.0137 mol) in a plastic beaker under magnetic stirring, followed by 11.458 g (0.0549 mol) of tetraethyl orthosilicate (Sinopharm, ≥99%). The mixture was homogenized to obtain a clear sol, which was then hydrolysed at ambient temperature overnight to evaporate partial water and ethanol. The gel was then heated at 100 °C in a convection oven to remove residue water and ethanol to reach the target gel molar composition by weighting. The viscous gel was transferred into Teflon-lined autoclaves and heated at 180 °C in a convection oven statically for 10 days. The solid product was recovered by centrifugation and washing three times with water (200 ml), ethanol (100 ml) and acetone (100 ml), then dried at 100 °C for 12 h and subsequently calcined at 600 °C for 12 h with a ramping time of 6 h. The yield of the product is 98% based on SiO2.

    The crystallization time could be shortened to 5 days by increasing the crystallization temperature to 190 °C with the same gel composition following the procedure described above. Further, by varying the molar ratio of Al2O3, ZMQ-1 zeolites with different framework Si/Al ratios were successfully synthesized using the gel molar compositions of 1.0SiO2:0.01Al2O3:0.25Tri-Cy-dC8(OH)2:10H2O and 1.0SiO2:0.005Al2O3:0.25 Tri-Cy-dC8(OH)2:10H2O. The solid product was recovered and processed according to the procedures described above.

    ZMQ-1 was also successfully obtained by using fumed silica (Macklin, AR) and aluminium sulfate octadecahydrate (Sinopharm, AR) as Si and Al sources with different H2O/SiO2 ratios under static and rotation crystallization modes. In a typical synthesis, a synthesis gel with molar composition of 1.0SiO2:0.02Al2O3:0.25Tri-Cy-dC8(OH)2:xH2O, in which x = 10, 16 and 30, was prepared and then subjected to heating in Teflon-lined autoclaves in a static or rotation oven for different durations. The product was collected and processed according to the procedures described above.

    Synthesis in the fluoride medium

    ZMQ-1 zeolite was obtained from the fluoride medium with a gel molar composition of 1.0SiO2:0.01Al2O3:0.25Tri-Cy-dC8(OH)2:0.5HF:10H2O. In a typical procedure, aluminosilicate gel was first prepared according to that of the synthesis in hydroxide medium at 180 °C. Then, 0.596 g (0.0275 mol) of HF (48%) was added to the gel and the resulting mixture was homogenized for 10 min using a spatula. (Caution! This must be done in a fume hood because of the toxic and corrosive gases generated during the mixing process.) The obtained viscous gel was transferred into a Teflon-lined autoclave and heated at 190 °C in a convection oven statically for 10–15 days. The solid product was washed and then calcined according to the procedures mentioned above. The yield of product is 96% based on SiO2.

    Zeolite synthesis overview

    The synthesis parameters and phases obtained are summarized in Supplementary Table 2. The first and foremost critical factor for the successful formation of ZMQ-1 phase is the introduction of Al, because pure silica syntheses in both hydroxide and fluoride media all produced unknown phase(s). Si/Al ratio in the synthetic gel ranging from 25 to 100 produced pure ZMQ-1 zeolites, but further increase and decrease of Si/Al directed to the unknown phase(s). Besides, heating temperature is vital for forming pure ZMQ-1 from aluminosilicate gel in hydroxide medium. Unknown phase(s) of little importance existed at a lower temperature of 180 °C, whereas they almost diminished at a higher temperature of 190 °C.

    Removal of phosphorous species

    Phosphorous species (oxides or phosphates) left in the calcined ZMQ-1 zeolites were removed by washing and ion exchanging with NH4Cl solution. Typically, 0.2 g of calcined zeolite powder was mixed with 10 g NH4Cl solution with a concentration of 1 mol l−1 in a sealed plastic bottle. The mixture was stirred in an oil bath at 80 °C overnight. The solid was collected by washing and filtration and then dried at 100 °C. The proton-form zeolite was obtained by calcining the above ammonium-form powder at 600 °C for 3 h with a 6-h ramp.

    Thermal and hydrothermal stability test

    The thermal stability of as-synthesized ZMQ-1 was tested in a muffle furnace at temperatures of 600 °C, 800 °C and 1,000 °C in air for 6 h, 1 h and 1 h, respectively, with a ramping rate of 6 h. The hydrothermal stability test was performed for the calcined zeolite (600 °C for 12 h) in a fixed-bed reactor at temperatures of 600 °C, 700 °C and 800 °C for 3 h with a relative humidity of 50% by purging deionized water with N2 flow. 0.1 g of zeolite powder was placed in a quartz reactor tube supported by quartz wool and then heated under N2 flow to the target temperature. Subsequently, deionized water was purged into the tube reactor by using a peristaltic pump to generate a mixture gas of N2 and steam. After the 3-h treatment finished, the system was cooled to room temperature and the zeolite powder was taken out for analysis.

    27Al NMR shows that ZMQ-1 calcined at 600 °C and 800 °C have similar resonances centred at about 58.3, 41.9 and −9.7 ppm (Supplementary Fig. 4a), which could be assigned to Al species in tetrahedrally coordinated framework sites, distorted tetrahedrally coordinated framework sites and octahedrally coordinated framework sites interacting with polymeric phosphate species34, respectively. By contrast, ZMQ-1 calcined at 1,000 °C shows the absence of resonance at about 58.3 ppm, indicating the possible disappearance of tetrahedrally coordinated framework Al. The prominent resonance at about 40.6 ppm is typically assigned to tetrahedrally coordinated Al in amorphous aluminophosphates formed by leached Al from the framework and P (ref. 35). Other resonances at 9.3 and −11.7 ppm could be assigned to octahedrally coordinated Al in aluminophosphates36. Ar adsorption–desorption isotherms and pore size distribution of the three calcined ZMQ-1 zeolites show that pore structure was preserved for zeolites calcined at 600 °C and 800 °C but almost disappeared for the 1,000 °C counterpart (Supplementary Fig. 4b–d and Supplementary Table 5), indicating the collapse of the structure, most probably because of the dealumination as corroborated by 27Al NMR spectra (Supplementary Fig. 4a). The thermal and hydrothermal stability of phosphorus-free ZMQ-1 (ZMQ-1(CW)) were also evaluated. ZMQ-1(CW) shows higher thermal stability up to 1,000 °C and hydrothermal stability comparable with that of phosphorus-containing counterpart (Extended Data Fig. 8e,f). The N2 adsorption–desorption data of ZMQ-1(CW) calcined at different temperatures show decreased gas adsorption amounts and micropore volumes, along with elevated heating temperatures (Supplementary Fig. 5a and Supplementary Table 6).

    General characterizations

    Unless otherwise stated, the physicochemical characterizations have been performed for ZMQ-1 samples synthesized in hydroxide medium. Laboratory PXRD patterns of the zeolites were collected in a Rigaku LabView diffractometer (CuKα, λ = 1.5418 Å). High-temperature in situ PXRD was carried out on the same diffractometer with a Reactor-X cell, which has a Si sample holder in a heated chamber with a window made of Be foil. PXRD patterns of selected samples were taken during a heating programme at 5 °C min−1 heating rate at 50 °C steps up to 1,000 °C. At each step, the temperature was held for 1 min for PXRD recording. The morphology and size of zeolite crystals were examined using a Hitachi S-4800 scanning electron microscope equipped with a cold field-emission gun with an accelerating voltage of 2 kV. The technical details of the experimental set-up used for in situ FTIR analysis in this work have been described elsewhere28. Thermogravimetric analysis of the as-synthesized zeolites was performed using a Rigaku TG-DTA8122 thermal analyser system in 50 ml min−1 air flow with a heating rate of 10 °C min−1 from 30 to 1,000 °C. The textural properties of the zeolites after OSDA removal were investigated by physisorption of Ar at 87 K using a Micromeritics 3Flex instrument equipped with a cryostat I accessory. The samples were outgassed under vacuum at 350 °C for 12 h before measurement. Analysis parameters were carefully selected to ensure proper equilibration of the data. The apparent surface area was calculated using the Brunauer–Emmett–Teller method and following the procedure recommended in ref. 37. The cumulative pore volume and pore size distributions were calculated by applying the kernel of (metastable) NLDFT adsorption isotherms considering a zeolitic surface and isolated cylinders as a pore model. Although this model gives the best approximation available so far, validating the main mode pore sizes, it is evident that it does not perfectly describe the complex pore structure and potential surface roughness of the ZMQ-1 zeolite, that is, main elongated cylindrical-like pores interconnected by means of smaller cylindrical windows. Micropore volumes were also determined using the same kernel. The calculations were carried out using the VersaWin 1.0 software package provided by QuantaTec (Anton Paar). For comparison purposes, the zeolites were also analysed on a QuantaTec Autosorb iQ sorption analyser at 87 K. Statistically consistent results were obtained. Contents of Si, Al and P were analysed by inductively coupled plasma optical emission spectroscopy using an Elementar Unicube apparatus. We performed CHN analysis using a Thermo Fisher iCAP PRO analyser.

    Solid-state NMR

    29Si, 27Al and 31P MAS NMR spectra were recorded on a Bruker Avance III HD 500 MHz spectrometer operating at 11.7 T using 4.0-mm rotors spun at νMAS = 14 kHz. The resonance frequencies of 29Si, 27Al and 31P were 99.4, 130.4 and 202.6 MHz, respectively. For 29Si MAS NMR, single-pulse duration of 2.33 µs corresponding to a flip angle of π/3 and a recycling delay of 20 s were used. 10,240 scans were acquired and chemical shifts were referenced towards teramethylsilane. For 27Al MAS NMR, single-pulse duration of 1.75 µs corresponding to a flip angle of π/12 and a recycling delay of 1 s were used. 4,096 scans were acquired and chemical shifts were referenced towards aluminium nitrate (Al(NO3)3). For 31P MAS NMR, single-pulse duration of 5.00 µs corresponding to a flip angle of π/2 and a recycling delay of 8 s were used. 1,600 scans were acquired and chemical shifts were referenced towards a saturated H3PO4 solution. 13C cross-polarization magic angle spinning (CPMAS) NMR spectra were recorded on a Bruker Avance III 600 MHz spectrometer at a resonance frequency of 150.9 MHz. 13C CPMAS NMR spectra were recorded using a 4-mm MAS probe and a spinning rate of 12 kHz. A contact time of 4 ms and a recycle delay of 2 s were used for the 13C CPMAS NMR measurement. The chemical shifts of 13C was referenced to teramethylsilane.

    STEM

    Samples for the STEM imaging were prepared by embedding a small amount (about 5 μg) of ZMQ-1 in LR White Resin within a gelatin capsule (size 00). The capsule was then hardened at 60 °C for 24 h. To create ultrathin sections with an estimated thickness of 50 nm, a Leica Ultracut UCT Ultramicrotome equipped with a 45° diamond knife from DiATOME was used. The sections were then transferred to Lacey carbon-supported copper grids. iDPC-STEM and ADF-STEM images of ZMQ-1 were obtained using a double aberration-corrected Themis Z TEM (Thermo Fisher Scientific) operated at an accelerating voltage of 300 kV. The images were acquired using a beam current of 10 pA, a convergence angle of 16 mrad and a dwell time of 5 μs. The iDPC-STEM images were formed using a segmented annular detector. A high-pass filter was applied to the iDPC-STEM images to reduce low-frequency contrast.

    3D ED

    Sample preparation

    The as-synthesized or calcined ZMQ-1 samples obtained by means of the hydroxide route were dispersed in acetone and then the suspensions were treated by ultrasonication for about 30 s. One droplet from the suspension was applied on a Lacey carbon-supported copper TEM grid for further cRED data collection.

    Data collection

    cRED data were collected at room temperature on a JEOL JEM-2100 transmission electron microscope (LaB6, 200 kV) using a high-speed Timepix hybrid camera (512 × 512 pixels, Amsterdam Scientific Instruments). The software Instamatic was used for the collection of cRED data. The crystals were rotated continuously at a rate of 0.45° s−1 during the data collection. The exposure time was 0.5 s per frame, which was integrated over 0.225° of reciprocal space. The electron flux density and camera length were 0.1 e Å2 s−1 and 25 cm, respectively.

    To locate the OSDAs in the pores, low-dose cRED datasets were collected under cryogenic conditions on a 300-kV Titan Krios G2 equipped with a Ceta-D camera. The prepared TEM grids with the as-synthesized ZMQ-1 were plunge-frozen in liquid ethane and then transferred into the Krios through cryogenic transfer. To minimize the unnecessary electron exposure, EPU-D was used to ensure that the sample was exposed to the electron beam only during the data collection. A flux density of 0.0025 e Å2 s−1 was used, which gave a cumulative fluence of 0.375 e Å−2 per dataset with a goniometer rotation range of 60°.

    Data processing and structure determination

    Rotation electron diffraction processing software (REDp) was first used to process cRED datasets to determine the unit cell and space group38. With the obtained unit cell and space group, cRED datasets were further indexed using X-ray detector software (XDS) to extract the reflection information39. For calcined ZMQ-1, the structure was solved from a single dataset using SHELXT. For as-synthesized ZMQ-1, three room-temperature datasets were merged based on their cross-correlation to improve completeness and I/σ(I). With the merged data, the average structure of as-synthesized ZMQ-1 was solved using SHELXT. The obtained structural models for both calcined and as-synthesized ZMQ-1 were further refined by SHELXL using the ShelXle GUI. All framework atoms were refined anisotropically. Soft restraints on bond distances and angles were applied in the refinement. Electron diffraction frames that show severe dynamical effects (that is, electron diffraction frames taken close to zone axes) were excluded during the data processing.

    Treatment of positional disorder in structure refinement of as-synthesized ZMQ-1

    During the structure refinement, positional disorder was found for two (Si29 and Si30) out of 30 symmetry-independent Si atoms. Two more Si peaks, assigned as Si31 and Si32, were located at a distance of 1.1 and 1.3 Å to Si29 and Si30, respectively. They were grouped together with the other framework atoms by applying a shared site occupancy with Si29 and Si30. Four oxygen atoms coordinated with these disordered Si atoms were found to be split into two positions each, as suggested by SHELXL during the refinement. The disordered oxygen atoms were divided into two groups, associated with either Si29 and Si30 (assigned as O63–66) or Si31 and Si32 (assigned as O67–70). The PART instruction was used to group the disordered atoms, designated as PART 1 for Si29,30 and O63–66 and PART 2 for Si31,32 and O67–70. The structure including the disordered parts and later with the OSDAs (see below) was refined using restraints on some Si–O bond lengths and bond angles. The occupancy of the two disordered parts was refined to be 56% for PART 1 and 44% for PART 2.

    Location of the OSDAs by low-dose cryo-cRED

    Although the cRED data collected at room temperature gave high resolution (0.80 Å), it was not possible to locate the OSDAs in the pores. Therefore we collected new cRED data under cryogenic conditions (90 K) using a 40 times lower electron flux density (0.0025 e Å−2 s−1) to mitigate electron-induced damage to the organic molecules (Supplementary Table 3). The low-dose cRED datasets were first indexed in P2/m using the XDS software to extract integrated intensities. To improve completeness and redundancy, ten datasets were scaled and merged into a single reflection file using XSCALE. Owing to the low dose, the data resolution was reduced to 1.08 Å, based on the CC1/2 value. To locate the atomic position of OSDAs, a difference electrostatic potential Fourier map was calculated using the high-resolution framework model of the as-synthesized ZMQ-1 with the positional disorder against the merged low-dose cRED dataset. Two symmetry-independent OSDAs could be located from the difference electrostatic potential map. The positions of the phosphonium cations and carbon atoms in the octamethylene chains could be located and refined using soft restraints. The occupancies of the OSDAs were also refined to be 1.00 for OSDA1 and 0.70(5) for OSDA2. The tricyclohexyl groups could not be located, presumably because of their flexibility.

    Catalytic cracking of VGO

    The ZMQ-1 samples used for catalytic tests were synthesized in hydroxide medium, USY and Beta were supplied by Zeolyst and UOP, respectively, and MCM-41 was purchased from Tianjin Yunli Chemical Company. The VGO was provided by Sinopec Qingdao Petrochemical Co., Ltd. and its composition and properties are listed in Supplementary Table 7. The catalytic test was carried out in a fixed-bed reactor at 500 °C. The aluminosilicate zeolite catalysts were pelletized, crushed and sieved to 0.18–0.25 mm (60–80 mesh), 0.25–0.38 mm (40–60 mesh) and 0.38–0.83 mm (20–40 mesh) to screen out the appropriate pellet size to mitigate the influence of diffusion limitations. The pellet size range 0.38–0.83 mm was then selected and used for the systematic tests based on the experiment results (Supplementary Tables 8 and 9). In a typical experiment, 1.0 g catalyst (20–40 mesh) was mixed with 4 g quartz sand (20–40 mesh) and loaded into a stainless/quartz reactor tube with a diameter of 20 mm. Before the test, the catalyst was treated at 500 °C in N2 flow at 40 ml min−1 for 20 min; then, the temperature was adjusted to the corresponding reaction temperatures. 1.7 g of VGO was injected at a constant rate on the catalyst bed with nitrogen as the inert carrier gas at a flow rate of 50 ml min−1. Gaseous products were collected in a sample bag. The liquid products (C5+) were collected in an ice bath downstream, which was kept at around 0 °C. The spent catalyst was stripped by nitrogen gas for about 30 min to recover the entrapped hydrocarbons. The gas and liquid products collected after the reaction were analysed using gas chromatography. Gases were analysed using an Agilent 7890A equipped with a HP-PLOT Al2O3 KCl column connected to a flame ionization detector for analysing C1–C6 hydrocarbons and Porapak-Q with a 5A molecular sieve column connected to thermal conductivity detectors for analysing H2, N2, CO, CO2, H2S and O2. Liquids were analysed using a Agilent 7890A equipped with DB-1 columns connected to a flame ionization detector for analysing gasoline, diesel and heavy oil components. After completing the VGO cracking performance test of the catalyst, the spent catalysts were regenerated at 650 °C under flowing air for calculating coke deposition and the regenerated catalysts were also applied for the next catalytic tests. The VGO conversion and product selectivity of dry gas (H2, H2S, CH4, C2H4, C2H6), liquefied petroleum gas (C3,4), gasoline (C5+, boiling point <200 °C) and diesel (C12+, boiling point is 200–365 °C) were defined as follows:

    $$\begin{array}{c}{\rm{Conversion}}\,( \% )=({W}_{{\rm{F}}}-{W}_{{\rm{H}}})/{W}_{{\rm{F}}}\times 100 \% \\ {\rm{Dry}}\,{\rm{gas}}\,{\rm{selectivity}}\,({\rm{wt}} \% )={W}_{1}/({W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}+{W}_{{\rm{c}}})\times 100 \% \\ {\rm{Liquefied}}\,{\rm{petroleum}}\,{\rm{gas}}\,{\rm{selectivity}}\,({\rm{wt}} \% )={W}_{2}/({W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}+{W}_{{\rm{c}}})\times 100 \% \\ {\rm{Gasoline}}\,{\rm{selectivity}}\,({\rm{wt}} \% )={W}_{3}/({W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}+{W}_{{\rm{c}}})\times 100 \% \\ {\rm{Diesel}}\,{\rm{selectivity}}\,({\rm{wt}} \% )={W}_{4}/({W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}+{W}_{{\rm{c}}})\times 100 \% \\ {\rm{Light}}\,{\rm{olefin}}\,{\rm{selectivity}}\,({\rm{wt}} \% )={W}_{{\rm{o}}}/({W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}+{W}_{{\rm{c}}})\times 100 \% \end{array}$$

    in which WF is mass of feed injected (g), WH is mass of heavy oil with a boiling point above 365 °C in product, which is treated as an unreacted heavy oil, W1 is mass of materials of dry gas components, W2 is mass of materials of liquefied petroleum gas components, W3 is mass of gasoline components, W4 is mass of diesel components, Wc is the mass of coke deposition and Wo is the mass of specific light olefin in all products excluding the heavy oil.

    Notably, for each VGO cracking run, a full mass balance was obtained. If the material balance was less than 95% or greater than 105%, the test was repeated. On the basis of the proposed possible reactions and detailed components obtained from gas chromatography, the mass balance is calculated as follows:

    $${\rm{Mass}}\,{\rm{balance}}=({W}_{1}+{W}_{2}+{W}_{3}+{W}_{4}+{W}_{{\rm{c}}}+{W}_{{\rm{H}}})/{W}_{{\rm{F}}}\times 100 \% $$

    We carried out the tests at different weight hour space velocities (WHSVs; Cat/Oil ratios) for both USY and ZMQ-1(CW), and the data are summarized in Supplementary Fig. 6a and Supplementary Table 10. Five WHSVs were tested, that is, 44, 87, 175, 350 and 874 h−1 (corresponding to Cat/Oil of 1.18, 0.59, 0.29, 0.15 and 0.06, respectively), by changing the loading amount of zeolites and keeping the feeding amount constant. With the decrease of the WHSV, the VGO conversion increases for both zeolites. For ZMQ-1(CW) and USY, a conversion of 98% was obtained at a WHSV of 87 h−1, but further decrease of the WHSV to 44 h−1 resulted in no obvious change in the conversion. Using the WHSV of 87 h−1, parallel experiments using the fresh ZMQ-1(CW) and USY were performed to eliminate the experimental errors (Supplementary Fig. 7 and Extended Data Table 1). The standard deviation values calculated for the heavy oil conversion rates and product selectivities from three runs are small, proving the high reliability and stability of the reaction system and analysis method we have used in this work. Then, using the WHSV of 87 h−1, we performed the consecutive reaction and regeneration tests for ZMQ-1(CW) to study the change in activity and deactivation behaviour. The data are shown in Supplementary Fig. 6b and Supplementary Table 11. For the five consecutive runs, no obvious deactivation was detected and the product distribution is almost consistent. The used catalyst was recovered and characterized. It shows that the crystallinity is decreased after reaction but the crystal structure is still retained (Supplementary Fig. 5).

    We performed five consecutive reactions without regeneration, which could be considered as quasi-time-on-stream tests, over USY and ZMQ-1(CW) zeolites, to study the deactivation profiles of the catalysts (Supplementary Fig. 8a). Both zeolites show monotonously decreasing activities along with incremental running times, that is, conversion declines from 95% and 96% to 64% and 60% for USY and ZMQ-1(CW), respectively, from the first to fifth run. ZMQ-1(CW) shows slightly lower activity after the initial reaction. Because the coke content could not be analysed using the present method, we further carried out incremental feeding from one to five times for each run, followed by reaction and regeneration, to obtain accumulated coke amount. The coke profiles show that ZMQ-1(CW) has larger accumulated coke, along with incremental feeding and reaction (Supplementary Fig. 8b), over that of USY, indicating that its intrinsic structure might be inducive to the large molecules formation. Comparing with reported optimized USY, Beta and extra-large-pore zeolites at comparable VGO conversion, ZMQ-1 shows higher yield towards fuels, preferentially for diesel, over that of ITT and JZO zeolites (Supplementary Table 12). Moreover, similar propylene yield is detected in ZMQ-1 and ITT.

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