RESUMO
Metal-organic frameworks (MOFs) have settled in the scientific community over the last decades as versatile materials with several applications. Among those, zeolitic imidazolate framework 8 (ZIF-8) is a well-known MOF that has been applied in various and diverse fields, from drug-delivery platforms to microelectronics. However, the complex role played by the reaction parameters in controlling the size and morphology of ZIF-8 particles is still not fully understood. Even further, many individual reports propose different nucleation and growth mechanisms for ZIF-8, thus creating a fragmented view for the behavior of the system. To provide a unified view, we have generated a comprehensive data set of synthetic conditions and their final outputs and applied machine learning techniques to analyze the data. Our approach has enabled us to identify the nucleation and growth mechanisms operating for ZIF-8 in a given sub-space of synthetic variables space (chemical space) and to reveal their impact on important features such as final particle size and morphology. By doing so, we draw connections and establish a hierarchy for the role of each synthetic variable and provide with rule of thumb for attaining control on the final particle size. Our results provide a unified roadmap for the nucleation and growth mechanisms of ZIF-8 in agreement with mainstream reported trends, which can guide the rational design of ZIF-8 particles which ultimately determine their suitability for any given targeted application. Altogether, our work represents a step forward in seeking control of the properties of MOFs through a deeper understanding of the rationale behind the synthesis procedures employed for their synthesis.
RESUMO
Semiconductor nanoparticles (SNPs) are excellent candidates for various applications in fields like solar cells, light emitting diodes or sensors. Their size strongly determines their properties, thus characterizing their size is crucial for applications. In most cases, they are included in complex matrices which make it difficult to determine their average diameter and statistical distribution. In this work, we present a non-destructive, cheap and in situ procedure to calculate particle size distributions (PSDs) of SNPs in different media based on deconvolution of the absorbance spectrum with a database of the absorbance spectra of SNPs with different sizes. The method was validated against the SNP sizes obtained from transmission microscopy images, showing excellent agreement between both distributions. In particular, CdS SNPs embedded in mesoporous thin films were analyzed in detail. Additional composite systems were studied in order to extend the method to SNPs in polymers or bacteria, proving that it applies to several SNPs in diverse matrices. The PSDs obtained from the proposed method do not show any statistical difference with the one derived from TEM images. Finally, a web app that implements the methodology of this work has been developed.
RESUMO
Free standing hierarchical bioglass scaffolds were prepared by the ISISA (ice-segregation-induced self-assembly) method. Commercial low-cost precursors such as Ludox® HS-40 and cow milk were employed as the source of SiO2 and biominerals (Ca(ii), P(v), Na(i) and K(i)), respectively. Then, in a single macroscopic piece, three levels of porosity coexist due to the simultaneous templating effect of ice (macropores), milk (50-200 nm mesopores) and the voids left between preformed Ludox® nano building blocks (2-5 nm mesopores). These low cost and green biological nanotemplates, coupled with the ISISA texturing method, allows the preparation of free standing bioglass monoliths, with hierarchical porosity. The effect of the main preparative variables on the final texture is explored; in vitro biomineralization revealed a well-distributed hydroxyapatite-like nanoparticulated layer within 24 h of exposure to a simulated body fluid.