Super-oxidation of silicon nanoclusters: magnetism and reactive oxygen species at the surface

Normal-incidence type solution immersed silicon (SIS) biosensor for ultra-sensitive, label-free detection of cardiac troponin I

Early diagnosis of acute myocardial infarction (AMI) significantly reduce the mortality rate and can be achieved via high-sensitive detection of AMI specific cardiac troponin I (cTnI) biomarker. Here, we present normal-incident type solution-immersed silicon (NI-SIS) ellipsometric biosensor, designed for ultra-high sensitive, high-throughput, label-free detection of the target protein. The NI-SIS sensors are equipped with a specially designed prism that maintains the angle of incidence close to the Brewster angle during operation, which significantly reduces SIS noise signals induced by the refractive index fluctuations of the surrounding medium, improves the signal-to-noise ratio, in-results lowers the detection limit. We applied NI-SIS biosensor for ultra-sensitive detection of cTnI biomarkers in human serum. The optimized sensor chip fabrication and detection operation procedures are proposed. The wide linear concentration ranges of fg/mL to ng/mL is achieved with the detection limit of 22.0 fg/mL of cTnI. The analytical correlation was assessed by linear regression analysis with the results of the Pathfast reference system. These impressive biosensing capabilities of NI-SIS technology have huge potentials for accurate detection of target species in different application areas, such as diagnosis, drug discovery, and food contaminations.

Method for Simultaneous Prediction of Atomic Structure and Stability of Nanoclusters in a Wide Area of Compositions

We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of structural similarities frequently observed in clusters of close compositions. The resulting speedup is up to 50 times compared to current methods. This enables first-principles studies of multicomponent clusters with full coverage of a wide range of compositions. As an example, we report an unprecedented first-principles global optimization of 315 Si _nO _m clusters with n ≤ 15 and m ≤ 20. The obtained map of Si-O cluster stability shows the existence of both expected (SiO₂) _n and unexpected (e.g., Si₄O₁₈) stable (magic) clusters, which can be important for a variety of applications.

Tetrahedral honeycomb surface reconstructions of quartz, cristobalite and stishovite

Crystalline silica (SiO₂) is a major material used in many technologies, yet the exact surface structures of silica polymorphs are still mostly unknown. Here we perform a comprehensive study of surface reconstructions of α-cristobalite (001), α-quartz (001) and stishovite (110) and (100) using evolutionary algorithm USPEX in conjunction with ab initio calculations. We found the well-known “dense surface” to be among low-energy reconstructions of α-quartz (001), as well as its previously proposed distorted version, which we call “shifted surface”. For cristobalite and stishovite we show the formation of reconstructions without dangling bonds which share common features with well-known “dense surface” of α-quartz (001). We call them “dense cristobalite” and “dense stishovite” – all of these have honeycomb arrangements of corner-sharing SiO₄-tetrahedra in the surface layers. These tetrahedral honeycombs have very low surface energies, and such tetrahedral surface pattern is observed even in stishovite (the bulk structure of which has SiO₆-octahedra, rather than SiO₄-tetrahedra).

The stability and unexpected chemistry of oxide clusters

The stability of Fe_mO_n clusters is determined by second energy differences.