Metal−organic frameworks (MOFs)-based sensors for monitoring toxic substances in wastewater have attracted great attention due to the efficient and reliable performance. Here, we has synthesized two novel zinc-based MOFs [Zn(ttb) 2 (H 2 O) 2 ] n (Zn1-ttb) and {[Zn(ttb) 2 ]•0.5CH 3 CN} n (Zn2-ttb) through changing the polarity of reaction solvents and finally obtained target 2D MOF material [Zn(ttb)(bdc) 0.5 ] n (Zn3-ttb-bdc) by successfully introducing an ancillary ligand H 2 bdc (Httb = 1-(triazo-1-ly)-4-(tetrazol-5-ylmethyl)benzene, H 2 bdc = 1,4-benzenedicarboxylic acid). As-prepared Zn3-ttb-bdc exhibits high water and chemical stability as well as excellent fluorescence property. Due to the −COOH binding sites from H 2 bdc, Zn3-ttb-bdc shows high sensitivity and a rapid luminescent response to a representative organic micropollutant trinitrophenol (TNP) and inorganic pollutants (Fe 3+ and Cr 2 O 7 2− ) in wastewater. The mechanisms of multifunctional detection abilities of Zn3-ttb-bdc toward different types of pollutants are further studied. This work presents the structural design in preparing MOF materials for multifunctional detection performance, thus opening new perspectives for emerging MOF-based sensors as environmental monitors.

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Partly interval-censored event time data arise naturally in medical, biological, sociological and demographic studies. In practice, some patients may be immune from the event of interest, invoking a cure model for survival analysis.Choosing an appropriate parametric distribution for the failure time of susceptible patients is an important step to fully structure the mixture cure model.In the literature, goodness-of-fit tests for survival models are usually restricted to uncensored or right-censored data. We fill in this gap by proposing a new goodness-of-fit test dealing with partly interval-censored data under mixture cure models. Specifically, we investigate whether a parametric distribution can fit the susceptible part by using a Cramér-von Mises type of test, and establish the asymptotic distribution of the test . Empirically, the critical value is determined from the bootstrap resamples. The proposed test, compared to the traditional leveraged bootstrap approach, yields superior practical results under various settings in extensive simulation studies. Two clinical data sets are analyzed to illustrate our method.