Host-guest complexes are emerging as powerful components in functional systems with applications ranging from materials to biomedicine. In particular, CB7 based host-guest complexes have received much attention for the controlled release of drugs due to the remarkable ability of CB7 toward binding input molecules in water with high affinity leading to displacement of CB7 from included pharmacophores (or from drug loaded porous particles). However, the release of bound guests from CB7 in response to endogenous biological molecules remains limited since the input biomolecule needs to have the appropriate chemical structure to bind tightly into the CB7 cavity. Herein we describe a synthetic transducer based on self-assembling DNA-small molecule chimeras (DCs) that is capable of converting a chosen biological input, adenosine triphosphate (ATP; that does not directly bind to the CB7 host) into functional displacement of a protein inhibitor that is bound within the CB7 host. Our system—which features the first example of a covalent CB-DNA conjugate—is highly modular and can be adapted to enable responsiveness to other biologically/clinically relevant stimuli via its split DNA aptamer architecture.

We describe a novel two-step method, starting from bulk silicon wafers, to construct DNA conjugated silicon nanoparticles (SiNPs). This method first utilizes reactive high-energy ball milling (RHEBM) to obtain alkene grafted SiNPs. The alkene moieties are subsequently reacted with commercially available thiol-functionalized DNA via thiol–ene click chemistry to produce SiNP DNA conjugates wherein the DNA is attached through a covalent thioether bond. Further, to show the utility of this synthetic strategy, we illustrate how these SiNP ODN conjugates can detect cancer-associated miR-21 via a fluorescence ON strategy. Given that an array of biological molecules can be prepared with thiol termini and that SiNPs are biocompatible and biodegradable, we envision that this synthetic protocol will find utility in salient SiNP systems for potential therapeutic and diagnostic applications.

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The main purpose of this study is to establish an effective landslide susceptibility zoning model and test whether underground mined areas and ground collapse in coal mine areas seriously affect the occurrence of landslides. Taking the Fenxi Coal Mine Area of Shanxi Province in China as the research area, landslide data has been investigated by the Shanxi Geological Environment Monitoring Center; adopting the 5-fold cross-validation method, and through Geostatistics analysis means the datasets of all non-landslides and landslides were divided into 80:20 proportions randomly for training and validating models. A set of 15 condition factors including terrain, geological, hydrological, land cover, and human engineering activity factors (distance to road, distance to mined area, ground collapse density) were selected as the evaluation indices to construct the susceptibility assessment model. Three machine learning algorithms for landslide susceptibility prediction (LSP) including C5.0 Decision Tree (C5.0), Random Forest (RF), and Support Vector Machine (SVM) have been selected and compared through the Areas under the Receiver Operating Characteristics (ROC) Curves (AUC), and several statistical estimates. The study revealed that for these three models the value range of prediction accuracies vary from 83.49 to 99.29% (in the training stage), and 62.26–73.58% (in the validation stage). In the two stages, AUCs are between 0.92 to 0.99 and 0.71 to 0.80 respectively. Using Jenks Natural Breaks algorithm, three LSPs levels are established as very low, low, medium, high, and very high probability of landslide by dividing the indices of the LSP. Compared with RF and SVM, C5.0 is considered better in five categories according to quantities and distribution of the landslides and their area percentage for different LSP zones. Four factors such as distance to road, lithology, profile curvature, and ground collapse density are the most suitable condition factors for LSP. The distance to mine area factor has a medium contribution and plays an obvious role in the occurrence of landslides in all the models. The result reveals that C5.0 possesses better prediction efficiency than RF and SVM, and underground mined area and ground collapse sifnigicantly affect significantly the occurrence of landslides in the Fenxi Coal Mine Area.

The development of methods to grow well-ordered chromophore thin films on solid substrates is of importance because such surface-associated arrays have potential applications in the generation of functional electronic and optical materials and devices. In this article, we demonstrate a straightforward layer-by-layer (LBL) supramolecular deposition strategy to prepare numerous layers (up to 19) of functionalized perylene diimide (PDI) chromophores built upon a covalent scaffolding multivalent porphyrin monolayer. Our thin film formation strategy employs water as the immersion solvent and exploits the β-cyclodextrin-adamantane host-guest couple in addition to PDI based aromatic stacking. Within the resultant film the porphyrin scaffold is oriented close to parallel to the glass substrate while the PDI chromophores are aligned closer to the surface normal. Together, the porphyrin monolayer and the multi-PDI layers exhibit a large absorption bandwidth in the visible spectrum. Importantly, because a self-assembly strategy was utilized, when a single monolayer of PDI is deposited on the porphyrin scaffolding layer, this PDI monolayer can be readily disassembled by washing with DMF leading to the regeneration of the porphyrin monolayer. The PDI thin film can subsequently be regrown from the regenerated porphyrin surface. The reported LBL strategy will be of broad interest for researchers developing well-organized chromophoric films and materials due to its simplicity as well as the added advantage of being performed in sustainable and cost-effective aqueous media.