RESUMO
The controlled synthesis of titanium-oxo clusters (TOCs) completely stabilized by organic dye ligands with high stability and superior light absorption remains a significant challenge. In this study, we report the syntheses of three atomically precise catechol (Cat)-functionalized TOCs, [Ti2(Cat)2(OEgO)2(OEgOH)2] (Ti2), [Ti8O5(Cat)9(iPrO)4(iPrOH)2] (Ti8), and [Ti16O8(OH)8(Cat)20]·H2O·PhMe (Ti16), using a solvent-induced strategy (HOEgOH = ethylene glycol; iPrOH = isopropanol; PhMe = toluene). Interestingly, the TiO core of Ti16 is almost entirely enveloped by catechol ligands, making it the first all-catechol-protected high-nuclearity TOC. In contrast, Ti2 and Ti8 have four weakly coordinated ethylene glycol ligands and six weakly coordinated iPrOH ligands, respectively, in addition to the catechol ligands. Ti16 is visually evident in its distinctively black appearance, which belongs to black TOCs (B-TOCs) and exhibits an ultralow optical band gap. Furthermore, Ti16 displays exceptional stability in various media/environments, including exposure to air, solvents, and both acidic and alkaline aqueous solutions due to its comprehensive protection by catechol ligands and rich intra-cluster supramolecular interactions. Ti16 has superior photoelectric response qualities and photothermal conversion capabilities compared to Ti2 and Ti8 due to its ultralow optical band gap and remarkable stability. This discovery not only represents a huge step forward in the creation of all-catecholate-protected B-TOCs with ultralow optical band gaps and outstanding stability, but it also gives key valuable mechanistic insights into their photothermal/electric applications.
RESUMO
BACKGROUND AND OBJECTIVE: Differentiating tropical infections are difficult due to its homogenous nature of clinical and laboratorial presentations among them. Sophisticated differential tests and prediction tools are better ways to tackle this issue. Here, we aimed to develop a clinician assisted decision making tool to differentiate the common tropical infections. METHODOLOGY: A cross sectional study through 9 item self-administered questionnaire were performed to understand the need of developing a decision making tool and its parameters. The most significant differential parameters among the identified infections were measured through a retrospective study and decision tree was developed. Based on the parameters identified, a multinomial logistic regression model and a machine learning model were developed which could better differentiate the infection. RESULTS: A total of 40 physicians involved in the management of tropical infections were included for need analysis. Dengue, malaria, leptospirosis and scrub typhus were the common tropical infections in our settings. Sodium, total bilirubin, albumin, lymphocytes and platelets were the laboratory parameters; and abdominal pain, arthralgia, myalgia and urine output were the clinical presentation identified as better predictors. In multinomial logistic regression analysis with dengue as a reference revealed a predictability of 60.7%, 62.5% and 66% for dengue, malaria and leptospirosis, respectively, whereas, scrub typhus showed only 38% of predictability. The multi classification machine learning model observed to have an overall predictability of 55-60%, whereas a binary classification machine learning algorithms showed an average of 79-84% for one vs other and 69-88% for one vs one disease category. CONCLUSION: This is a first of its kind study where both statistical and machine learning approaches were explored simultaneously for differentiating tropical infections. Machine learning techniques in healthcare sectors will aid in early detection and better patient care.