Customizing CO2 Electroreduction by Pulse-Induced Anion Enrichment.

Electrochemically converting CO2 into specified high-value products is critical for carbon neutral economics. However, governing the product distribution of the CO2 electroreduction on Cu-based catalysts remains challenging. Herein, we put forward an anion enrichment strategy to efficiently dictate the route of *CO reduction by a pulsed electrolysis strategy. Upon periodically applying a positive potential on the cathode, the anion concentration in the vicinity of the electrode increases apparently. By adopting KF, KCl, and KHCO3 as electrolytes, the dominant CO2 electroreduction product on commercial Cu foil can be tuned into CO (53% ± 2.5), C2+ (76.6 ± 2.1%), and CH4 (42.6 ± 2.1%) under pulsed electrolysis. Notably, one can delicately tailor the ratios of CO/CH4, CH4/C2+, and C2+/CO by simply changing the composition of the electrolyte. Density functional theory calculations demonstrate that locally enriched anions can affect the key CO2RR intermediates in different ways owing to their specific electronegativity and volume, which leads to the distinct selectivity. The present study highlights the importance of tuning ionic species at the electrode-electrolyte interface for customizing the CO2 electroreduction products.

Optimizing the detection of emerging infections using mobility-based spatial sampling.

Background: Timely and precise detection of emerging infections is crucial for effective outbreak management and disease control. Human mobility significantly influences infection risks and transmission dynamics, and spatial sampling is a valuable tool for pinpointing potential infections in specific areas. This study explored spatial sampling methods, informed by various mobility patterns, to optimize the allocation of testing resources for detecting emerging infections. Methods: Mobility patterns, derived from clustering point-of-interest data and travel data, were integrated into four spatial sampling approaches to detect emerging infections at the community level. To evaluate the effectiveness of the proposed mobility-based spatial sampling, we conducted analyses using actual and simulated outbreaks under different scenarios of transmissibility, intervention timing, and population density in cities. Results: By leveraging inter-community movement data and initial case locations, the proposed case flow intensity (CFI) and case transmission intensity (CTI)-informed sampling approaches could considerably reduce the number of tests required for both actual and simulated outbreaks. Nonetheless, the prompt use of CFI and CTI within communities is imperative for effective detection, particularly for highly contagious infections in densely populated areas. Conclusions: The mobility-based spatial sampling approach can substantially improve the efficiency of community-level testing for detecting emerging infections. It achieves this by reducing the number of individuals screened while maintaining a high accuracy rate of infection identification. It represents a cost-effective solution to optimize the deployment of testing resources, when necessary, to contain emerging infectious diseases in diverse settings.

Large-scale spatiotemporal deep learning predicting urban residential indoor PM2.5 concentration.

Indoor PM2.5 pollution is one of the leading causes of death and disease worldwide. As monitoring indoor PM2.5 concentrations on a large scale is challenging, it is urgent to assess population-level exposure and related health risks to develop an easy-to-use and generalized model to predict indoor PM2.5 concentrations and spatiotemporal variations at the global level. Existing machine learning models of indoor PM2.5 are prone to deliver single-point predictions, and their input strategies are not widely applicable. Here, we developed a Bayesian neural network (BNN) model for predicting the distribution of daily average urban residential PM2.5 concentration based on multiple data sources available from nationwide comprehensive sensor-monitoring records in China. The BNN model showed good performance with a 10-fold cross-validation R2 of 0.70, mean-absolute-error of 9.45 µg/m3, root-mean-square error of 13.3 µg/m3, and 95 % prediction interval coverage of 85 %. To demonstrate the application process, this model was applied to predict indoor PM2.5 concentrations on a large spatiotemporal scale. Our modeled population-weighted annual indoor PM2.5 concentration for China in 2019 was 22.8 µg/m3, far exceeding the WHO standard. The validity of the model at the population level can be further bolstered, making it valuable for assessing and managing indoor air pollution-related health risks.

An analysis of impact load and fragmentation dimension to explore energy dissipation patterns in coal crushing.

This research delineates the energy dissipation characteristics in coal crushing under impact loads, leveraging the capabilities of Separated Hopkinson Pressure Bar experimental system. A meticulous examination of both burst-prone and non-burst-prone coal samples during destruction processes was undertaken to decipher the dynamic compression mechanical attributes from perspectives of energy and fragmentatio's fractal dimensions. Burst-prone coal showcases a more pronounced escalation in fragmentation work in comparison to non-burst-prone samples, thereby illustrating a perceptible strain-rate dependent effect correlating with enhanced strain rates. Additionally, it was observed that incident, reflected, and transmitted energy trajectories for both sample categories follow an approximately linear ascendancy, albeit exhibiting diverse magnitudes. Burst-prone coal manifests a more rapid and focused energy growth compared to its non-burst-prone counterpart. When subjected to impact loads, a notable trend was discerned where the fragmentation's fractional dimension escalated persistently with both the incident energy and the crushing work, portraying a prominent growth effect. The insights garnered from this study pave the way for distinguishing between impacted and unimpacted coal samples using energy perspectives and fragmentation's fractal dimensions.

Effects of public-health measures for zeroing out different SARS-CoV-2 variants.

Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.

Modulating Electron Density of Ni-N-C Sites by N-doped Ni for Industrial-level CO2 Electroreduction in Acidic Media.

Electro-chemically reducing CO2 in a highly acidic medium is promising for addressing the issue of carbonate accumulation. However, the hydrogen evolution reaction (HER) typically dominates the acidic CO2 reduction. Herein, we construct an efficient electro-catalyst for CO formation based on a core-shell structure, where nitrogen-doped Ni nanoparticles coexist with nitrogen-coordinated Ni single atoms. The optimal catalyst demonstrates a significantly improved CO faradaic efficiency (FE) of 96.7 % in the acidic electrolyte (pH=1) at an industrial-scale current density of 500 mA cm-2 . Notably, the optimal catalyst maintains a high FE of CO exceeding 90 % (current density=500 mA cm-2 ) in the electrolyte with a wide pH range from 0.67 to 14. In-situ spectroscopic characterization and density functional theory calculations show that the local electron density of Ni-N-C sites is enhanced by N-doped Ni particles, which facilitates the formation of *COOH intermediate and the adsorption of *CO. This study demonstrates the potential of a hybrid metal/Ni-N-C interface in boosting acidic CO2 electro-reduction.

Enhanced CO Affinity on Cu Facilitates CO2 Electroreduction toward Multi-Carbon Products.

Electrochemical CO2 reduction reaction (CO2 RR) is a promising strategy for waste CO2 utilization and intermittent electricity storage. Herein, it is reported that bimetallic Cu/Pd catalysts with enhanced *CO affinity show a promoted CO2 RR performance for multi-carbon (C2+) production under industry-relevant high current density. Especially, bimetallic Cu/Pd-1% catalyst shows an outstanding CO2 -to-C2+ conversion with 66.2% in Faradaic efficiency (FE) and 463.2 mA cm-2 in partial current density. An increment in the FE ratios of C2+ products to CO  for Cu/Pd-1% catalyst further illuminates a preferable C2+ production. In situ Raman spectra reveal that the atop-bounded CO is dominated by low-frequency band CO on Cu/Pd-1% that leads to C2+ products on bimetallic catalysts, in contrast to the majority of high-frequency band CO on Cu that favors the formation of CO. Density function theory calculation confirms that bimetallic Cu/Pd catalyst enhances the *CO adsorption and reduces the Gibbs free energy of the CC coupling process, thereby favoring the formation of C2+ products.

Single-Atom Anchored Curved Carbon Surface for Efficient CO2 Electro-Reduction with Nearly 100% CO Selectivity and Industrially-Relevant Current Density.

Although single metal atoms on porous carbons (PCs) are widely used in electrochemical CO2 reduction reaction, these systems have long relied on flat graphene-based models, which are far beyond reality because of abundant curved structures in PCs; the effect of curved surfaces has long been ignored. In addition, the selectivity generally decreases under high current density, which severely limits practical application. Herein, theoretical calculations reveal that a single-Ni-atom on a curved surface can simultaneously enhance the total density of states around Fermi level and decrease the energy barrier for *COOH formation, thereby enhancing catalytic activity. This work reports a rational molten salt approach for preparing PCs with ultra-high specific surface area of up to 2635 m2 g-1 . As determined by cutting-edge techniques, a single Ni atom on a curved carbon surface is obtained and used as a catalyst for electrochemical CO2 reduction. The CO selectivity reaches up to 99.8% under industrial-level current density of 400 mA cm-2 , outperforming state-of-the-art PC-based catalysts. This work not only offers a new method for the rational synthesis of single atom catalysts with strained geometry to host rich active sites, but also provides in-depth insights for the origin of catalytic activity of curved structure-enriched PC-based catalysts.

Anchoring ultra-small molybdenum oxide species on covalent triazine frameworks for efficient electrochemical nitrogen fixation.

We report a smart ion-exchange strategy to anchor molybdenum oxide particles on charge-modulated conjugated triazine frameworks (Mo/CTF-I) for electrochemically fixing nitrogen. The strong interaction between MoOx and CTF-I is conducive to the activation of the inert N2 molecule in the electro-chemical process. As a result, 5% Mo/CTF-I exhibited an excellent faradaic efficiency of 27.3% and an NH3 yield rate of 7.23 µg h-1 mgcat.-1 at -0.405 V vs. RHE in 0.1 M KOH, surpassing most previous reports.

Dynamic Reconstructed RuO2 /NiFeOOH with Coherent Interface for Efficient Seawater Oxidation.

Developing efficient oxygen evolution reaction (OER) electrocatalysts for seawater electrolysis is still a big challenge. Herein, a facile one-pot approach is reported to synthesize RuO2 -incorporated NiFe-metal organic framework (RuO2 /NiFe-MOF) with unique nanobrick-nanosheet heterostructure as precatalyst. Driven by electric field, the RuO2 /NiFe-MOF dynamically reconstructs into RuO2 nanoparticles-anchored NiFe oxy/hydroxide nanosheets (RuO2 /NiFeOOH) with coherent interface, during which the dissolution and redeposition of RuO2 are witnessed. Owing to the synergistic interaction between RuO2 and NiFeOOH, the as-reconstructed RuO2 /NiFeOOH exhibits outstanding alkaline OER activity with an ultralow overpotential of 187.6 mV at 10 mA cm-2 and a small Tafel slope of 31.9 mV dec-1 and excellent durability at high current densities of 840 and 1040 mA cm-2 in 1 m potassium hydroxide (KOH). When evaluated for seawater oxidation, the RuO2 /NiFeOOH only needs a low overpotential of 326.2 mV to achieve 500 mA cm-2 and can continuously catalyze OER at 500 mA cm-2 for 100 h with negligible activity degradation. Density function theory calculations reveal that the presence of strong interaction and enhanced charge transfer along the coherent interface between RuO2 and NiFeOOH ensures improved OER activity and stability.

Quantifying knowledge from the perspective of information structurization.

Scientific literature, as the major medium that carries knowledge between scientists, exhibits explosive growth in the last century. Despite the frequent use of many tangible measures, to quantify the influence of literature from different perspectives, it remains unclear how knowledge is embodied and measured among tremendous scientific productivity, as knowledge underlying scientific literature is abstract and difficult to concretize. In this regard, there has laid a vacancy in the theoretical embodiment of knowledge for their evaluation and excavation. Here, for the first time, we quantify the knowledge from the perspective of information structurization and define a new measure of knowledge quantification index (KQI) that leverages the extent of disorder difference caused by hierarchical structure in the citation network to represent knowledge production in the literature. Built upon 214 million articles, published from 1800 to 2021, KQI is demonstrated for mining influential classics and laureates that are omitted by traditional metrics, thanks to in-depth utilization of structure. Due to the additivity of entropy and the interconnectivity of the network, KQI assembles numerous scientific impact metrics into one and gains interpretability and resistance to manipulation. In addition, KQI explores a new perspective regarding knowledge measurement through entropy and structure, utilizing structure rather than semantics to avoid ambiguity and attain applicability.

Intraday adaptation to extreme temperatures in outdoor activity.

Linkages between climate and human activity are often calibrated at daily or monthly resolutions, which lacks the granularity to observe intraday adaptation behaviors. Ignoring this adaptation margin could mischaracterize the health consequences of future climate change. Here, we construct an hourly outdoor leisure activity database using billions of cell phone location requests in 10,499 parks in 2017 all over China to investigate the within-day outdoor activity rhythm. We find that hourly temperatures above 30 °C and 35 °C depress outdoor leisure activities by 5% (95% confidence interval, CI 3-7%) and by 13% (95% CI 10-16%) respectively. This activity-depressing effect is larger than previous daily or monthly studies due to intraday activity substitution from noon and afternoon to morning and evening. Intraday adaptation is larger for locations and dates with time flexibility, for individuals more frequently exposed to heat, and for parks situated in urban areas. Such within-day adaptation substantially reduces heat exposure, yet it also delays the active time at night by about half an hour, with potential side effect on sleep quality. Combining empirical estimates with outputs from downscaled climate models, we show that unmitigated climate change will generate sizable activity-depressing and activity-delaying effects in summer when projected on an hourly resolution. Our findings call for more attention in leveraging real-time activity data to understand intraday adaptation behaviors and their associated health consequences in climate change research.

Central nitrogen vacancies in polymeric carbon nitride for boosted photocatalytic H2O2 production.

Polymeric carbon nitride (PCN) with vacancies usually exhibits distinguished mass transfer efficiency, outstanding carrier kinetics and excellent photoactivity. Previous studies have revealed the effect of edge vacancies in heptazine units of PCN; however, the roles of central nitrogen vacancies are scarcely investigated. Herein, central nitrogen vacancies polymeric carbon nitride (PCN-NVC) is rationally prepared for photocatalytic H2O2 production with a rate of 25.1 umol/h (λ > 420 nm), which is 3.5 times than that of pristine PCN. Photoelectronic measurements reveal that the central nitrogen vacancies optimize the kinetic process of electron-hole pairs. Density functional theory (DFT) calculations disclose that PCN-NVC displays lower O2 adsorption energy, thereby accelerating the OOH* formation and decreasing the H2O2 generation energy barrier. This work not only provides a strategy for constructing central nitrogen vacancies polymeric carbon nitrogen, but also affords a deep understanding of its roles in photocatalytic H2O2 production.

Measuring daily-life fear perception change: A computational study in the context of COVID-19.

COVID-19, as a global health crisis, has triggered the fear emotion with unprecedented intensity. Besides the fear of getting infected, the outbreak of COVID-19 also created significant disruptions in people's daily life and thus evoked intensive psychological responses indirect to COVID-19 infections. In this study, we construct a panel expressed fear database tracking the universe of social media posts (16 million) generated by 536 thousand individuals between January 1st, 2019 and August 31st, 2020 in China. We employ deep learning techniques to detect expressions of fear emotion within each post, and then apply topic model to extract the major topics of fear expressions in our sample during the COVID-19 pandemic. Our unique database includes a comprehensive list of topics, not being limited to post centering around COVID-19. Based on this database, we find that sleep disorders ("nightmare" and "insomnia") take up the largest share of fear-labeled posts in the pre-pandemic period (January 2019-December 2019), and significantly increase during the COVID-19. We identify health and work-related concerns are the two major sources of non-COVID fear during the pandemic period. We also detect gender differences, with females having higher fear towards health topics and males towards monetary concerns. Our research shows how applying fear detection and topic modeling techniques on posts unrelated to COVID-19 can provide additional policy value in discerning broader societal concerns during this COVID-19 crisis.

Scientific X-ray: Scanning and quantifying the idea evolution of scientific publications.

The rapid development of modern science nowadays makes it rather challenging to pick out valuable ideas from massive scientific literature. Existing widely-adopted citation-based metrics are not adequate for measuring how well the idea presented by a single publication is developed and whether it is worth following. Here, inspired by traditional X-ray imaging, which returns internal structure imaging of real objects along with corresponding structure analysis, we propose Scientific X-ray, a framework that quantifies the development degree and development potential for any scientific idea through an assembly of 'X-ray' scanning, visualization and parsing operated on the citation network associated with a target publication. We pick all 71,431 scientific articles of citation counts over 1,000 as high-impact target publications among totally 204,664,199 publications that cover 16 disciplines spanning from 1800 to 2021. Our proposed Scientific X-ray reproduces how an idea evolves from the very original target publication all the way to the up to date status via an extracted 'idea tree' that attempts to preserve the most representative idea flow structure underneath each citation network. Interestingly, we observe that while the citation counts of publications may increase unlimitedly, the maximum valid idea inheritance of those target publications, i.e., the valid depth of the idea tree, cannot exceed a limit of six hops, and the idea evolution structure of any arbitrary publication unexceptionally falls into six fixed patterns. Combined with a development potential index that we further design based on the extracted idea tree, Scientific X-ray can vividly tell how further a given idea presented by a given publication can still go from any well-established starting point. Scientific X-ray successfully identifies 40 out of 49 topics of Nobel prize as high-potential topics by their prize-winning papers in an average of nine years before the prizes are released. Various trials on articles of diverse topics also confirm the power of Scientific X-ray in digging out influential/promising ideas. Scientific X-ray is user-friendly to researchers with any level of expertise, thus providing important basis for grasping research trends, helping scientific policy-making and even promoting social development.

Protocol for fabrication and evaluation of oxide-modified Cu foils as heterostructured electrodes for electrochemical CO2 reduction.

Heterostructured catalysts based on Cu and oxides are promising for the efficient conversion of CO2 to multi-carbon products. In this protocol, we describe the fabrication and characterization of Cu/oxide heterostructured catalysts and the evaluation approach of electrochemical CO2 reduction reaction (CO2RR) performance in an H-type cell. We also provide the details of in situ surface-enhanced Raman measurement and theoretical calculations. The protocol can be useful for constructing self-supported electrodes and assessing the CO2RR performance of as-fabricated electrodes. For complete details on the use and execution of this protocol, please refer to Li et al. (2022).

Hetero-Interfaces on Cu Electrode for Enhanced Electrochemical Conversion of CO2 to Multi-Carbon Products.

Electrochemical CO2 reduction reaction (CO2RR) to multi-carbon products would simultaneously reduce CO2 emission and produce high-value chemicals. Herein, we report Cu electrodes modified by metal-organic framework (MOF) exhibiting enhanced electrocatalytic performance to convert CO2 into ethylene and ethanol. The Zr-based MOF, UiO-66 would in situ transform into amorphous ZrOx nanoparticles (a-ZrOx), constructing a-ZrOx/Cu hetero-interface as a dual-site catalyst. The Faradaic efficiency of multi-carbon (C2+) products for optimal UiO-66-coated Cu (0.5-UiO/Cu) electrode reaches a high value of 74% at - 1.05 V versus RHE. The intrinsic activity for C2+ products on 0.5-UiO/Cu electrode is about two times higher than that of Cu foil. In situ surface-enhanced Raman spectra demonstrate that UiO-66-derived a-ZrOx coating can promote the stabilization of atop-bound CO* intermediates on Cu surface during CO2 electrolysis, leading to increased CO* coverage and facilitating the C-C coupling process. The present study gives new insights into tailoring the adsorption configurations of CO2RR intermediate by designing dual-site electrocatalysts with hetero-interfaces.

Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories.

Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42-62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.

Anchoring Effect of Organosilanes on Hierarchical ZSM-5 Zeolite for Catalytic Fast Pyrolysis of Cellulose to Aromatics.

As an essential chemical feedstock, aromatics can be obtained from biomass by catalytic fast pyrolysis (CFP) technology, in which diffusion limitation is still a problem. In this study, several ZSM-5 zeolites with intercrystal stacking macropores were synthesized by adding organosilanes (OSAs) with different alkyl chain groups. Due to the structure-directing effect of the OSA, the prepared ZSM-5 zeolites possess a larger external surface area and pore volume than Blank-Z5. Moreover, the pore size is related to the extent of anchoring of the OSA and silicon-aluminum species in the zeolite precursor. Pyridine Fourier transform infrared (Py-FTIR) and NH3-temperature-programmed desorption (TPD) analyses show that the obtained ZSM-5 zeolites have a higher Brønsted acidity and total number of acid sites. In addition, excessive addition of OSA is not conducive to the growth of ZSM-5 zeolites. The catalytic performance of the synthesized ZSM-5 zeolites was evaluated by Py-GC/MS. The larger external surface area and pore volume improve the accessibility of the acid sites and thus promote the conversion of biomass into aromatics.

Optimized Sequencing Adaptors Enable Rapid and Real-Time Metagenomic Identification of Pathogens during Runtime of Sequencing.

BACKGROUND: Metagenomic next-generation sequencing (mNGS) offers the promise of unbiased detection of emerging pathogens. However, in indexed sequencing, the sequential paradigm of data acquisition, demultiplexing, and analysis restrain read assignment in advance and real-time analysis, resulting in lengthy turnaround time for clinical metagenomic detection. METHODS: We described the utility of internal-index adaptors with different lengths of barcode in multiplex sequencing. The base composition for each position within these adaptors was well-balanced to ensure nucleotide diversity and optimal sequencing performance and to achieve the early assignment of reads by first sequencing the barcodes. Combined with an automated library preparation device, we delivered a rapid and real-time bioinformatics pathogen identification solution for the Illumina NextSeq platform. The diagnostic performance was evaluated by testing 153 lower respiratory tract specimens using mNGS in comparison to culture, 16S/internal transcribed spacer amplicon sequencing, and additional PCR-based tests. RESULTS: By calculating the average F1 scores of all read lengths under different threshold values, we established the optimal threshold for pathogens identification, and found that 36 bp was the optimal shortest read length for rapid mNGS analysis. Rapid detection had a negative percentage agreement and positive percentage agreement of 100% and 85.1% for bacteria and 97.4% and 80.3% for fungi, when compared to a composite standard. The rapid mNGS solution enabled accurate pathogen identification in about 9.1 to 10.1 h sample-to-answer turnaround time. CONCLUSIONS: Optimized internal index adaptors combined with a real-time analysis pipeline provide a potential tool for a first-line test in critically ill patients.