Integrating Photoactive Ligands into Crystalline Ultrathin 2D Metal-Organic Framework Nanosheets for Efficient Photoinduced Energy Transfer.

3D metal-organic frameworks (MOFs) have gained attention as heterogeneous photocatalysts due to their porosity and unique host-guest interactions. Despite their potential, MOFs face challenges, such as inefficient mass transport and limited light penetration in photoinduced energy transfer processes. Recent advancements in organic photocatalysis have uncovered a variety of photoactive cores, while their heterogenization remains an underexplored area with great potential to build MOFs. This gap is bridged by incorporating photoactive cores into 2D MOF nanosheets, a process that merges the realms of small-molecule photochemistry and MOF chemistry. This approach results in recyclable heterogeneous photocatalysts that exhibit an improved mass transfer efficiency. This research demonstrates a bottom-up synthetic method for embedding photoactive cores into 2D MOF nanosheets, successfully producing variants such as PCN-641-NS, PCN-643-NS, and PCN-644-NS. The synthetic conditions were systematically studied to optimize the crystallinity and morphology of these 2D MOF nanosheets. Enhanced host-guest interactions in these 2D structures were confirmed through various techniques, particularly solid-state NMR studies. Additionally, the efficiency of photoinduced energy transfer in these nanosheets was evidenced through photoborylation reactions and the generation of reactive oxygen species (ROS).

Magneto-Nanomechanical Array Biosensor for Ultrasensitive Detection of Oncogenic Exosomes for Early Diagnosis of Cancers.

Exosomes are a class of nanoscale vesicles secreted by cells, which contain abundant information closely related to parental cells. The ultrasensitive detection of cancer-derived exosomes is highly significant for early non-invasive diagnosis of cancer. Here, an ultrasensitive nanomechanical sensor is reported, which uses a magnetic-driven microcantilever array to selectively detect oncogenic exosomes. A magnetic force, which can produce a far greater deflection of microcantilever than that produced by the intermolecular interaction force even with very low concentrations of target substances, is introduced. This method reduced the detection limit to less than 10 exosomes mL-1 . Direct detection of exosomes in the serum of patients with breast cancer and in healthy people showed a significant difference. This work improved the sensitivity by five orders of magnitude as compared to that of traditional nanomechanical sensing based on surface stress mode. This method can be applied parallelly for highly sensitive detection of other microorganisms (such as bacteria and viruses) by using different probe molecules, which can provide a supersensitive detection approach for cancer diagnosis, food safety, and SARS-CoV-2 infection.

Integrated Photocatalytic Reduction and Oxidation of Perfluorooctanoic Acid by Metal-Organic Frameworks: Key Insights into the Degradation Mechanisms.

The high porosity and tunability of metal-organic frameworks (MOFs) have made them an appealing group of materials for environmental applications. However, their potential in the photocatalytic degradation of per- and polyfluoroalkyl substances (PFAS) has been rarely investigated. Hereby, we demonstrate that over 98.9% of perfluorooctanoic acid (PFOA) was degraded by MIL-125-NH2, a titanium-based MOF, in 24 h under Hg-lamp irradiation. The MOF maintained its structural integrity and porosity after three cycles, as indicated by its crystal structure, surface area, and pore size distribution. Based on the experimental results and density functional theory (DFT) calculations, a detailed reaction mechanism of the chain-shortening and H/F exchange pathways in hydrated electron (eaq-)-induced PFOA degradation were revealed. Significantly, we proposed that the coordinated contribution of eaq- and hydroxyl radical (•OH) is vital for chain-shortening, highlighting the importance of an integrated system capable of both reduction and oxidation for efficient PFAS degradation in water. Our results shed light on the development of effective and sustainable technologies for PFAS breakdown in the environment.

Assembling Phenothiazine into a Porous Coordination Cage to Improve Its Photocatalytic Efficiency for Organic Transformations.

Photo-catalysis by small-molecules is often limited by catalyst degradation and low electron-transfer efficiency. Herein we report a stable N-phenyl-phenothiazine (PTH)-derived porous coordination cage (PCC) as a highly efficient photocatalyst. By the incorporation of the photocatalytic PTH moiety into a PCC, aggregation-induced quenching (AIQ) was shown to be reduced. An improvement in catalyst stability was discovered, ascribed to the synergistic effects of the PTH moieties. The catalyst, operating through a photolytic single-electron transfer, was utilized for photo-catalyzed dehalogenation and borylation. Evaluation of the catalytic mechanism in the borylation reaction showed that the improved performance results from the more efficient formation of the electron donor-acceptor (EDA) complex with the cage. This discovery provides a potential strategy to improve the photophysical properties and stabilities of small-molecule organic photocatalysts via supramolecular chemistry.

Superparamagnetic iron oxide-gold nanoparticles conjugated with porous coordination cages: Towards controlled drug release for non-invasive neuroregeneration.

This paper reports a smart intracellular nanocarrier for sustainable and controlled drug release in non-invasive neuroregeneration. The nanocarrier is composed by superparamagnetic iron oxide-gold (SPIO-Au) core-shell nanoparticles (NPs) conjugated with porous coordination cages (PCCs) through the thiol-containing molecules as bridges. The negatively charged PCC-2 and positively charged PCC-3 are compared for intracellular targeting. Both types result in intracellular targeting via direct penetration across cellular membranes. However, the pyrene (Py)-PEG-SH bridge enabled functionalization of SPIO-Au NPs with PCC-3 exhibits higher interaction with PC-12 neuron-like cells, compared with the rhodamine B (RhB)-PEG-SH bridge enabled case and the stand-alone SPIO-Au NPs. With neglectable toxicities to PC-12 cells, the proposed SPIO-Au-RhB(Py)-PCC-2(3) nanocarriers exhibit effective drug loading capacity of retinoic acid (RA) at 13.505 µg/mg of RA/NPs within 24 h. A controlled release of RA is achieved by using a low-intensity 525 nm LED light (100% compared to 40% for control group within 96 h).

Precise Spatial-Designed Metal-Organic-Framework Nanosheets for Efficient Energy Transfer and Photocatalysis.

High-efficiency photocatalysis in metal-organic frameworks (MOF) and MOF nanosheets (NSs) are often limited by their short-lived charge separation as well as self-quenching. Here, we propose to use the energy-transfer process (EnT) to increase charge separation, thus enhancing the catalytic performance of a series of MOF NSs. With the use of NS, the photocatalyst can also be well isolated to reduce self-quenching. Tetrakis(4-carboxyphenyl) porphyrin (H4 TCPP) and 1,3,6,8-tetrakis(p-benzoic acid)pyrene (H4 TBAPy) linkers were chosen as the acceptor and donor moieties, respectively. Accounting for the precise spatial design afforded by the MOF NSs, the donor and acceptor moieties could be closely positioned on the NSs, allowing for an efficient EnT process as well as a high degree of site isolation. Two templates, donor-on-acceptor NS and acceptor-on-donor NS catalysts, were successfully synthesized, and the results show that the second one has much enhanced catalytic performances over the first one due to site-isolated active photocatalysts.

Modular Total Synthesis in Reticular Chemistry.

The idea of modularity in organic total synthesis has promoted the construction of diverse targeted natural products by varying the building blocks and assembly sequences. Yet its utilization has been mainly limited to the synthesis of molecular compounds based on covalent bonds. In this work, we expand the conceptual scope of modular synthesis into framework materials, which bridges metal- and covalent organic frameworks (MOFs and COFs) hierarchically in reticular chemistry. While the assembly sequences are determined by the coordination or the covalent bond strengths, a modular synthesis strategy which progressively links simple building blocks into increasingly sophisticated superstructures was reported. As a result, a series of hierarchical COF-on-MOF structures with architectural intricacy were obtained through sequence-defined reactions of diverse building blocks. The tunability of spatial apportionment, compositions, and functionality was successfully managed in these framework materials. To the best of our knowledge, this is the first report on the synthesis of COF@MOF composites and also the first discovery of controlled COF alignment. This generalizable modularity strategy will not only accelerate the discovery of multicomponent framework materials by the hierarchical assembly of MOFs and COFs but also offer a predictable retrosynthetic route to smart materials with unusual tunability owing to the diverse inorganic or organic building units.

Rapid Generation of Hierarchically Porous Metal-Organic Frameworks through Laser Photolysis.

Hierarchically porous metal-organic frameworks (HP-MOFs) facilitate mass transfer due to mesoporosity while preserving the advantage of microporosity. This unique feature endows HP-MOFs with remarkable application potential in multiple fields. Recently, new methods such as linker labilization for the construction of HP-MOFs have emerged. To further enrich the synthetic toolkit of MOFs, we report a controlled photolytic removal of linkers to create mesopores within microporous MOFs at tens of milliseconds. Ultraviolet (UV) laser has been applied to eliminate "photolabile" linkers without affecting the overall crystallinity and integrity of the original framework. Presumably, the creation of mesopores can be attributed to the missing-cluster defects, which can be tuned through varying the time of laser exposure and ratio of photolabile/robust linkers. Upon laser exposure, MOF crystals shrank while metal oxide nanoparticles formed giving rise to the HP-MOFs. In addition, photolysis can also be utilized for the fabrication of complicated patterns with high precision, paving the way towards MOF lithography, which has enormous potential in sensing and catalysis.

Modular Programming of Hierarchy and Diversity in Multivariate Polymer/Metal-Organic Framework Hybrid Composites.

The idea that complex systems have a hierarchical arrangement has been widely observed on various scales. In this work, we introduce the concept of modular programming, which emphasizes isolating the functionality of a system into independent, interchangeable modules, to tailor the hierarchy and diversity in these complex systems. Guided by modular programming, a system with multiple compatible components, including modules A, B, C, and so forth, can be constructed and subsequently modified into modules A', B', C', and so forth independently. As a proof of concept, a series of multivariate hierarchical metal-organic frameworks (MOFs) with various compositions, ratios, and distributions were prepared as a compatible system. Sequential click reactions and acid treatments can be utilized to selectively modify a certain modular MOF into a polymer, while other modular MOFs either remain in their original state or dissolve upon treatment. As a result, a series of polymer/MOF composites that traditionally have been viewed as incompatible can be prepared with tailored properties and behaviors. The resulting polymer/MOF hierarchical composites represent a unique porous composite material which contains functional groups and metal clusters with controllable compositions and distribution, tunable hierarchically porous structures, and tailored diversity within one framework. This general synthesis approach guided by modular programming not only provides a facile method to tailor hierarchy and diversity in multivariate systems but also enables the investigation into hierarchy and its structured control flow, which is a critical design feature of future materials for their fast adaptivity and responses to variable environmental conditions.

Imprinted Apportionment of Functional Groups in Multivariate Metal-Organic Frameworks.

Sophisticated chemical processes widely observed in biological cells require precise apportionment regulation of building units, which inspires researchers to develop tailorable architectures with controllable heterogeneity for replication, recognition and information storage. However, it remains a substantial challenge to endow multivariate materials with internal sequences and controllable apportionments. Herein, we introduce a novel strategy to manipulate the apportionment of functional groups in multivariate metal-organic frameworks (MTV-MOFs) by preincorporating interlocked linkers into framework materials. As a proof of concept, the imprinted apportionment of functional groups within ZIF-8 was achieved by exchanging imine-based linker templates with original linkers initially. The removal of linker fragments by hydrolysis can be achieved via postsynthetic labilization, leading to the formation of architectures with controlled heterogeneity. The distributions of functional groups in the resulting imprinted MOFs can be tuned by judicious control of the interlocked chain length, which was further analyzed by computational methods. This work provides synthetic tools for precise control of pore environment and functionality sequences inside multicomponent materials.

Three Landmark Optimization Strategies for Mobile Robot Visual Homing.

Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision. In this paper, we propose three strategies to solve this problem. We use scale-invariant feature transform (SIFT) features as natural landmarks, and the proposed strategies can optimize the landmark distribution without over-eliminating landmarks or increasing calculation amount. Experiments on both panoramic image databases and a real mobile robot have verified the effectiveness and feasibility of the proposed strategies.

Deep-Learning-Based Nanomechanical Vibration for Rapid and Label-Free Assay of Epithelial Mesenchymal Transition.

Cancer is a profound danger to our life and health. The classification and related studies of epithelial and mesenchymal phenotypes of cancer cells are key scientific questions in cancer research. Here, we investigated cancer cell colonies from a mechanical perspective and developed an assay for classifying epithelial/mesenchymal cancer cell colonies using the biomechanical fingerprint in the form of "nanovibration" in combination with deep learning. The classification method requires only 1 s of vibration data and has a classification accuracy of nearly 92.5%. The method has also been validated for the screening of anticancer drugs. Compared with traditional methods, the method has the advantages of being nondestructive, label-free, and highly sensitive. Furthermore, we proposed a perspective that subcellular structure influences the amplitude and spectrum of nanovibrations and demonstrated it using experiments and numerical simulation. These findings allow internal changes in the cell colony to be manifested by nanovibrations. This work provides a perspective and an ancillary method for cancer cell phenotype diagnosis and promotes the study of biomechanical mechanisms of cancer progression.

Numerical Analysis of Linearly Implicit Methods for Discontinuous Nonlinear Gurtin-MacCamy Model.

In this study, we study a nonlinear age-structured population models with discontinues mortality and fertility rates, motivated by the fact that different maturation period may cause the significant difference in rates. We develop a novel numerical method with two-layer boundary conditions, the linearly implicit θ-methods on a special mesh. With a uniform boundedness analysis of numerical solutions, the finite time convergence is proved piecewisely according to the fundamental approach for the smooth rates. For juvenile-adult models, the existence of numerical endemic equilibrium is determined by a numerical basic reproduction function, which converges to the exact one with accuracy of order 1. Moreover, it is shown that for juvenile-adult models, the global stability of the disease-free equilibrium and the local stability of the endemic equilibrium are approximately exhibited by the numerical processes. Finally, some numerical experiments on the Logistic models and tadpoles-frogs models illustrate the verification and the efficiency of our results.

Relay-type sensing mode: A strategy to push the limit on nanomechanical sensor sensitivity based on the magneto lever.

Ultrasensitive molecular detection and quantization are crucial for many applications including clinical diagnostics, functional proteomics, and drug discovery; however, conventional biochemical sensors cannot satisfy the stringent requirements, and this has resulted in a long-standing dilemma regarding sensitivity improvement. To this end, we have developed an ultrasensitive relay-type nanomechanical sensor based on a magneto lever. By establishing the link between very weak molecular interaction and five orders of magnitude larger magnetic force, analytes at ultratrace level can produce a clearly observable mechanical response. Initially, proof-of-concept studies showed an improved detection limit up to five orders of magnitude when employing the magneto lever, as compared with direct detection using probe alone. In this study, we subsequently demonstrated that the relay-type sensing mode was universal in application ranging from micromolecule to macromolecule detection, which can be easily extended to detect enzymes, DNA, proteins, cells, viruses, bacteria, chemicals, etc. Importantly, we found that, sensitivity was no longer subject to probe affinity when the magneto lever was sufficiently high, theoretically, even reaching single-molecule resolution. Electronic Supplementary Material: Supplementary material (experimental section) is available in the online version of this article at 10.1007/s12274-022-5049-0.

Nanomechanical assay for ultrasensitive and rapid detection of SARS-CoV-2 based on peptide nucleic acid.

The massive global spread of the COVID-19 pandemic makes the development of more effective and easily popularized assays critical. Here, we developed an ultrasensitive nanomechanical method based on microcantilever array and peptide nucleic acid (PNA) for the detection of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) RNA. The method has an extremely low detection limit of 0.1 fM (105 copies/mL) for N-gene specific sequence (20 bp). Interestingly, it was further found that the detection limit of N gene (pharyngeal swab sample) was even lower, reaching 50 copies/mL. The large size of the N gene dramatically enhances the sensitivity of the nanomechanical sensor by up to three orders of magnitude. The detection limit of this amplification-free assay method is an order of magnitude lower than RT-PCR (500 copies/mL) that requires amplification. The non-specific signal in the assay is eliminated by the in-situ comparison of the array, reducing the false-positive misdiagnosis rate. The method is amplification-free and label-free, allowing for accurate diagnosis within 1 h. The strong specificity and ultra-sensitivity allow single base mutations in viruses to be distinguished even at very low concentrations. Also, the method remains sensitive to fM magnitude lung cancer marker (miRNA-155). Therefore, this ultrasensitive, amplification-free and inexpensive assay is expected to be used for the early diagnosis of COVID-19 patients and to be extended as a broad detection tool. Electronic Supplementary Material: Supplementary material (experimental section, N gene sequences and all nucleic acid sequences used in the study, Figs. S1-S6, and Tables S1-S3) is available in the online version of this article at 10.1007/s12274-022-4333-3.

HEAR4Health: a blueprint for making computer audition a staple of modern healthcare.

Recent years have seen a rapid increase in digital medicine research in an attempt to transform traditional healthcare systems to their modern, intelligent, and versatile equivalents that are adequately equipped to tackle contemporary challenges. This has led to a wave of applications that utilise AI technologies; first and foremost in the fields of medical imaging, but also in the use of wearables and other intelligent sensors. In comparison, computer audition can be seen to be lagging behind, at least in terms of commercial interest. Yet, audition has long been a staple assistant for medical practitioners, with the stethoscope being the quintessential sign of doctors around the world. Transforming this traditional technology with the use of AI entails a set of unique challenges. We categorise the advances needed in four key pillars: Hear, corresponding to the cornerstone technologies needed to analyse auditory signals in real-life conditions; Earlier, for the advances needed in computational and data efficiency; Attentively, for accounting to individual differences and handling the longitudinal nature of medical data; and, finally, Responsibly, for ensuring compliance to the ethical standards accorded to the field of medicine. Thus, we provide an overview and perspective of HEAR4Health: the sketch of a modern, ubiquitous sensing system that can bring computer audition on par with other AI technologies in the strive for improved healthcare systems.

Reducing Compaction Temperature of Asphalt Mixtures by GNP Modification and Aggregate Packing Optimization.

Compaction of hot mix asphalt (HMA) requires high temperatures in the range of 125 to 145 °C to ensure the fluidity of asphalt binder and, therefore, the workability of asphalt mixtures. The high temperatures are associated with high energy consumption, and higher NOx emissions, and can also accelerate the aging of asphalt binders. In previous research, the authors have developed two approaches for improving the compactability of asphalt mixtures: (1) addition of Graphite Nanoplatelets (GNPs), and (2) optimizing aggregate packing. This research explores the effects of these two approaches, and the combination of them, on reducing compaction temperatures while the production temperature is kept at the traditional levels. A reduction in compaction temperatures is desired for prolonging the paving window, extending the hauling distance, reducing the energy consumption for reheating, and for reducing the number of repairs and their negative environmental and safety effects, by improving the durability of the mixtures. A Superpave asphalt mixture was chosen as the control mixture. Three modified mixtures were designed, respectively, by (1) adding 6% GNP by the weight of binder, (2) optimizing aggregate packing, and (3) combining the two previous approaches. Gyratory compaction tests were performed on the four mixtures at two compaction temperatures: 135 °C (the compaction temperature of the control mixture) and 95 °C. A method was proposed based on the gyratory compaction to estimate the compaction temperature of the mixtures. The results show that all the three methods increase the compactability of mixtures and thus significantly reduce the compaction temperatures. Method 3 (combining GNP modification and aggregate packing optimization) has the most significant effect, followed by method 1 (GNP modification), and method 2 (aggregate packing optimization).

Determination of 12 anti-obesity drugs in human plasma by a 96-well protein precipitation plate using HPLC-MS.

An analytical method was developed and validated for the simultaneous determination of 12 anti-obesity drugs (methylephedrine (MER), amphetamine (AMP), fenfluramine (FEN), bupropion (BUP), fluoxetine (FLU), sibutramine (SIBU), bisacodyl (BISA), bumetanide (BUM), lovastatin (LOVA), simvastatin (SIM), rimonabant (RIMO), and fenofibrate (FENO)) in human plasma by a 96-well protein precipitation plate combined with high-performance liquid chromatography-mass spectrometry (HPLC-MS/MS). The 96-well protein precipitation plate was chosen for simultaneous pretreatment of large sample volumes, making the whole process more efficient and faster. Drugs were separated on an Agilent Poroshell 120 EC-C18 column, and detected by MS/MS under multiple reaction monitoring (MRM) mode. The developed method was validated in terms of linearity, matrix effect, accuracy and precision. A good linearity was obtained in the range of 0.1-20.0 ng mL-1 for fenfluramine, bupropion, fluoxetine, sibutramine, bisacodyl, and rimonabant; and 0.5-20.0 ng mL-1 for methylephedrine, amphetamine, bumetanide, lovastatin, simvastatin, and fenofibrate with a correlation coefficient above 0.995. The method was fully validated with an acceptable accuracy of 75.63-108.21%, matrix effect of 80.41-117.71% except for fenofibrate (76.07% at low concentration levels), and precision of 0.32-13.12%. Owing to the advantages of simple operation, high accuracy and sensitivity, this method is suitable for the rapid and simultaneous detection of 12 anti-obesity drugs in human plasma, providing support for clinically monitoring the development of adverse reactions and guiding the rational and appropriate use of weight-loss drugs for obese people.

Magnetic nanocomposite hydrogel with tunable stiffness for probing cellular responses to matrix stiffening.

As cells have the capacity to respond to their mechanical environment, cellular biological behaviors can be regulated by the stiffness of extracellular matrix. Moreover, biological processes are dynamic and accompanied by matrix stiffening. Herein, we developed a stiffening cell culture platform based on polyacrylamide-Fe3O4 magnetic nanocomposite hydrogel with tunable stiffness under the application of magnetic field. This platform provided a wide range of tunable stiffness (∼0.3-20 kPa) covering most of human tissue elasticity with a high biocompatibility. Overall, the increased magnetic interactions between magnetic nanoparticles reduced the pore size of the hydrogel and enhanced the hydrogel stiffness, thereby facilitating the adhesion and spreading of stem cells, which was attributed to the F-actin assembly and vinculin recruitment. Such stiffening cell culture platform provides dynamic mechanical environments for probing the cellular response to matrix stiffening, and benefits studies of dynamic biological processes. STATEMENT OF SIGNIFICANCE: Cellular biological behaviors can be regulated by the stiffness of extracellular matrix. Moreover, biological processes are dynamic and accompanied by matrix stiffening. Herein, we developed a stiffening cell culture platform based on polyacrylamide/Fe3O4 magnetic nanocomposite hydrogels with a wide tunable range of stiffness under the application of magnetic field, without adversely affecting cellular behaviors. Such matrix stiffening caused by enhanced magnetic interaction between magnetic nanoparticles under the application of the magnetic field could induce the morphological variations of stem cells cultured on the hydrogels. Overall, our stiffening cell culture platform can be used not only to probe the cellular response to matrix stiffening but also to benefit various biomedical studies.

Capturing Time Dynamics From Speech Using Neural Networks for Surgical Mask Detection.

The importance of detecting whether a person wears a face mask while speaking has tremendously increased since the outbreak of SARS-CoV-2 (COVID-19), as wearing a mask can help to reduce the spread of the virus and mitigate the public health crisis. Besides affecting human speech characteristics related to frequency, face masks cause temporal interferences in speech, altering the pace, rhythm, and pronunciation speed. In this regard, this paper presents two effective neural network models to detect surgical masks from audio. The proposed architectures are both based on Convolutional Neural Networks (CNNs), chosen as an optimal approach for the spatial processing of the audio signals. One architecture applies a Long Short-Term Memory (LSTM) network to model the time-dependencies. Through an additional attention mechanism, the LSTM-based architecture enables the extraction of more salient temporal information. The other architecture (named ConvTx) retrieves the relative position of a sequence through the positional encoder of a transformer module. In order to assess to which extent both architectures can complement each other when modelling temporal dynamics, we also explore the combination of LSTM and Transformers in three hybrid models. Finally, we also investigate whether data augmentation techniques, such as, using transitions between audio frames and considering gender-dependent frameworks might impact the performance of the proposed architectures. Our experimental results show that one of the hybrid models achieves the best performance, surpassing existing state-of-the-art results for the task at hand.