Direct detection of stringent alarmones (pp)pGpp using malachite green.

The alarmone (p)ppGpp serves as the signalling molecule for the bacterial universal stringent response and plays a crucial role in bacterial virulence, persistence, and stress adaptation. Consequently, there is a significant focus on developing new drugs that target and modulate the levels of (p)ppGpp as a potential strategy for controlling bacterial infections. However, despite the availability of various methods for detecting (p)ppGpp, a simple and straightforward detection method is needed. In this study, we demonstrated that malachite green, a well-established compound used for phosphate detection, can directly detect (p)ppGpp and its analogues esp., pGpp. By utilizing malachite green, we identified three new inhibitors of the hydrolase activity of SpoT, one of the two RelA-SpoT homolog (RSH) proteins responsible for making and hydrolyzing (p)ppGpp in Escherichia coli. These findings highlight the convenience and practicality of malachite green, which can be widely employed in high-throughput studies to investigate (pp)pGpp in vitro and discover novel regulators of RSH proteins.

Experimental exploration and DFT analysis of the kinetics and mechanism of malachite green photodegradation catalyzed by polyaniline-copper oxide nanocomposite.

CONTEXT AND RESULTS: A nanocomposite photocatalyst consisting of polyaniline (PANI) and copper oxide (CuO) was successfully synthesized through an in-situ polymerization approach using aniline as the precursor. The synthesized nanocomposite was characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), UV-visible spectroscopy (UV-Vis), determination of the point of zero charge (pHPZC), and scanning electron microscopy (SEM). The photocatalytic efficiency of the PANI-CuO nanocomposite was evaluated in the context of photodegrading Malachite Green (MG) dye under visible light. Malachite Green, a synthetic dye commonly used in the textile and aquaculture industries, is a significant contaminant due to its toxic, mutagenic, and carcinogenic properties, making its removal from water resources crucial for environmental and human health. Distilled water artificially contaminated with MG dye was used as the medium for testing. The parameters influencing the photodegradation efficiency were comprehensively investigated. These parameters included catalyst dosage, reaction time, initial dye concentration, and pH. The results of this study indicate that the degradation efficiency of MG dye displayed an upward trend with time, catalyst dosage, and pH while exhibiting a converse relationship with the initial dye concentration. A degradation rate of 97% was achieved with an initial concentration of 20 mg L-1, employing a catalyst dose of 1.6 g L-1 at pH 6 for a reaction time of 180 min. Furthermore, the reusability of the catalyst was assessed, revealing consistent performance over five consecutive cycles. COMPUTATIONAL AND THEORETICAL TECHNIQUES: Density functional theory (DFT) was employed to optimize the structures of PANI, PANI-CuO, and their respective complexes formed through dye interaction, employing Gaussian software. These calculations employed the B3LYP/6-311G + + (d,p) basis set in an aqueous environment with water serving as the solvent. The kinetics of Malachite Green degradation were analyzed using both first and second-order kinetic models.

Enhanced catalytic performance and pH stability of Streptomyces Laccase Y230R and its degradation of malachite green.

The escalating threat of malachite green (MG) pollution poses significant risks to ecosystems. Saturation mutation targeting Tyr230 of small laccase (SLAC) from Streptomyces coelicolor yielded Y230R, exhibiting a remarkable 104 % increase in specific activity. Notably, this mutation achieved dual enhancements in both activity and pH stability. Molecular dynamics simulation revealed higher structural stability of Y230R compared to wild-type (WT) across varying pH levels. The increased count of hydrogen bonds in Y230R compared to WT may be contribute to its stability. Y230R demonstrated superior catalytic efficiency (67.0 %) in MG decolorization, maintaining over 90 % activity after 30 min incubation in MG solution (500 mg/L), highlighting enhanced tolerance compared to WT. Molecular docking analysis attributed the differential catalytic effects on MG and ABTS to structural disparities and hydrogen bonding. Y230R stands as a promising composite mutant for future laccase engineering and industrial applications.

Combined Electrochemical Deposition and Photo-Reduction to Fabricate SERS-Active Silver Substrates: Characterization and Application for Malachite Green Detection in Aquaculture Water.

This research introduces a novel approach using silver (Ag) nanostructures generated through electrochemical deposition and photo-reduction of Ag on fluorine-doped tin oxide glass substrates (denoted as X-Ag-AgyFTO, where 'X' and 'y' represent the type of light source and number of deposited cycles, respectively) for surface-enhanced Raman spectroscopy (SERS). This study used malachite green (MG) as a Raman probe to evaluate the enhancement factors (EFs) in SERS-active substrates under varied fabrication conditions. For the substrates produced via electrochemical deposition, we determined a Raman EF of 6.15 × 104 for the Ag2FTO substrate. In photo-reduction, the impact of reductant concentration, light source, and light exposure duration were examined on X-Ag nanoparticle formation to achieve superior Raman EFs. Under optimal conditions (9.0 mM sodium citrate, 460 nm blue-LED at 10 W for 90 min), the combination of blue-LED-reduced Ag (B-Ag) and an Ag2FTO substrate (denoted as B-Ag-Ag2FTO) exhibited the best Raman EF of 2.79 × 105. This substrate enabled MG detection within a linear range of 0.1 to 1.0 µM (R2 = 0.98) and a detection limit of 0.02 µM. Additionally, the spiked recoveries in aquaculture water samples were between 90.0% and 110.0%, with relative standard deviations between 3.9% and 6.3%, indicating the substrate's potential for fungicide detection in aquaculture.

Artificial neural network modelling and experimental investigations of malachite green adsorption on novel carboxymethyl cellulose/ß-cyclodextrin/nickel cobaltite composite.

This study presents a novel polymer nanocomposite based on carboxymethyl cellulose and ß-cyclodextrin crosslinked with succinic acid (CMC-SA-ß-CD) containing nickel cobaltite (NCO) nano-reinforcement. Various analytical techniques have been employed to investigate the structural, thermal, and morphological features of the resulting nanocomposite. The CMC-SA-ß-CD/NCO nanocomposite has been utilized as an adsorbent for the removal of bisphenol-A (BPA, R% <40 %), malachite green (MG, R% > 75 %)), and Congo red (CR, no adsorption) from the synthetic wastewater. The study systematically explored the impact of various parameters on the adsorption process, and the interactions between MG and CMC-SA-ß-CD/NCO were discussed. The adsorption data were fitted to different models to elucidate the kinetics and thermodynamics of the adsorption process. An artificial neural network (ANN) analysis was employed to train the experimental dataset for predicting adsorption outcomes. Despite a low BET surface area (0.798 m2 g-1), CMC-SA-ß-CD/NCO was found to exhibit high MG adsorption capacity. CMC-SA-ß-CD/NCO exhibited better MG adsorption performance at pH 5.5, 40 mg L-1 MG dye concentration, 170 min equilibrium time, 20 mg CMC-SA-ß-CD/NCO dose with more than 90 % removal efficiency. Moreover, the thermodynamic studies suggest that the adsorption of MG was exothermic with ΔH° value -9.93 ± 0.76 kJ mol-1. The isotherm studies revealed that the Langmuir model was the best model to describe the adsorption of MG on CMC-SA-ß-CD/NCO indicating monolayer surface coverage with Langmuir adsorption capacity of 182 ± 4 mg g-1. The energy of adsorption (11.4 ± 0.8 kJ mol-1) indicated chemisorption of MG on the composite surface. The kinetics studies revealed that the pseudo-first-order model best described the adsorption kinetics with q e  = 86.7 ± 2.9 mg g-1. A good removal efficiency (>70 %) was retained after five regeneration reuse cycles. The ANN-trained data showed good linearity between predicted and actual data for the adsorption capacity (R-value>0.99), indicating the reliability of the prediction model. The developed nanocomposite, composed predominantly of biodegradable material, is facile to synthesize and exhibited excellent monolayer adsorption of MG providing a new sustainable adsorbent for selective MG removal.

Potassium salts activated lignin-based biochar as an effective adsorbent for malachite green adsorption.

Herein, a series of lignin-based porous carbons (LC) were prepared from sulfonated lignin through a simple and environmentally-friendly one-pot activated carbonization together with various potassium compounds as activators, and used for malachite green (MG) adsorption. The results showed that the prepared biochar, especially after K2CO3 activation, exhibited a honeycomb profile with large surface area (2107.6 m2/g) and high total pore volume (1.1591 cm3/g), having excellent efficiency for MG adsorption, and the biggest adsorption capacity was 2970.0 mg/g. The kinetic study together with thermodynamic analysis indicated that the adsorption of MG by LC-K2CO3 conformed to pseudo-second-order model and the adsorption process was spontaneous, feasible, and endothermic. Moreover, LC-K2CO3 also displayed good stability and selectivity, and can selective separate the cationic dye from binary-dye system. Furthermore, the adsorption mechanism proposed in this work manifested that the high-efficient MG adsorption by LC-K2CO3 was a result of multiple actions including hydrogen bonding, electrostatic attraction, π-π interaction and n-π interaction as well as physical absorption. The work not only provide a fundamental theory for dye removal from wastewater, but offered a new insight for lignin valorization.

New bacterial strains isolated from Tunisian biotopes: A sustainable enzymatic approach for decolorization and detoxification of Congo Red and Malachite Green.

Seven bacterial strains, isolated from various Tunisian biotopes, were investigated for Congo Red (CR) and Malachite Green (MG) decolorization. The isolated strains underwent morphological and biochemical tests, including assessments for antibiotic sensitivity as well as biofilm formation. One selected strain, ST11, was partially identified as Paenibacillus sp. strain ST11. The newly isolated crude bacterial filtrates (NICBFs) effectively decolorized CR and MG. Specifically, six and seven NICBFs were found to be effective for degrading CR (150 mg l-1) and MG (50 mg l-1), respectively. Under non-optimized conditions, CR and MG could be decolorized up to 80% within 6-12 h. The degradation products of CR and MG, characterized by UV-visible and FT-IR techniques, demonstrated both decolorization and transformation, highlighting the role of enzymes in dye degradation. Phytotoxicity and cytotoxicity studies evaluated the impact of treated and untreated CR and MG. Some NICBFs showed promise as powerful biological tools, reducing and sometimes detoxifying CR and MG, commonly used as fertilizers. The potential applications of these NICBFs in decolorization and bioremediation of dye-rich textile effluents were explored. The screening also identified environmentally friendly, cost-effective bacterial strains adaptable to various conditions through phytotoxicity and cytotoxicity studies.

Development and validation of rapid screening of 192 veterinary drug residues in aquatic products using HPLC-HRMS coupled with QuEChERS.

The presence of veterinary drug residues in aquatic products represents a significant challenge to food safety. The current detection methods, limited in both scope and sensitivity, underscore the urgent need for more advanced techniques. This research introduces a swift and potent screening technique using high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) and a refined QuEChERS protocol, allowing simultaneous qualitative and semi-quantitative analysis of 192 residues. A comprehensive database, employing full scan mode and data-dependent secondary mass spectroscopy, enhances screening accuracy. The method involves efficient extraction using 90% acetonitrile, dehydration with Na2SO4, and acetic acid, followed by cleanup using dispersive solid-phase extract sorbent primary secondary amine. It is suitable for samples with varying fat content, offering detection limits ranging from 0.5 to 10 µg/kg, high recovery rates (60-120%), and low relative standard deviations (<20%). Practical application has validated its effectiveness for multi-residue screening, marking a significant advancement in food safety evaluation.

A ratiometric fluorescent sensor based on S-doped BCNO quantum dots and Au nanoclusters combined with 3D-printing portable device for the detection of malachite green.

Sulfur-doped BCNO quantum dots (S-BCNO QDs) emitting green fluorescence were prepared by elemental doping method. The ratiometric fluorescence probe with dual emissions was simply established by mixed S-BCNO QDs with gold nanoclusters (GSH-Au NCs). Because the emission spectrum of Au NCs (donor) at 615 nm overlapped well with the ultraviolet absorption of malachite green (MG), fluorescence resonance energy transfer (FRET) can be achieved. When the concentration of MG increased, the fluorescence intensity (F495) of S-BCNO QDs decreased slowly, while the fluorescence intensity (F615) of Au NCs decreased sharply. The fluorescence intensity ratio of F615/F495 decreased with the increase of MG. By plotting the F615/F495 values against MG concentration, a sensitive and rapid detection of MG was possible with a wide detection range (0.1-50 µM) and a low detection limit of 10 nM. Due to the accompanying fluorescence color change from pink to blue-green, it can be used for visual detection. A three dimensional-printing device utilizing digital image colorimetry to capture color changes through the built-in camera, enables quantitative detection of MG with a good linearity between the values of red/green ratio and MG concentrations at the range 1-50 µM. This sensing platform had a range of advantages, including high cost-effectiveness, portability, ease of operation, and high sensitivity. Furthermore, the sensing platform was successfully applied to the detection of MG in real water sample and fish samples, thereby verifying the reliability and effectiveness of this sensing platform in water quality monitoring and food safety.

Linear and nonlinear regression modelling of industrial dye adsorption using nanocellulose@chitosan nanocomposite beads.

Nanocellulose@chitosan (nc@ch) composite beads were prepared via coagulation technique for the elimination of malachite green dye from aqueous solution. As malachite green dye is highly used in textile industries for dyeing purpose which after usage shows fatal effects to the ecosystems and human beings also. In this study the formulated nanocellulose@chitosan composite beads were characterized by Particle size analysis (PSA), Field emission scanning electron microscopy (FESEM), Fourier-transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analysis were done to evaluate nanoparticles size distribution, morphological behaviour, functional group entities and degree of crystallinity of prepared beads. The nanocomposite beads adsorption performance was investigated for malachite green (MG) dye and BET analysis were also recorded to know about porous behaviour of the nanocomposite beads. Maximum removal of malachite green (MG) dye was found to be 72.0 mg/g for 100 ppm initial dye concentration. For accurate observations linear and non-linear modelling was done to know about the best-fitted adsorption model during the removal mechanism of dye molecules, on evaluating it has been observed that Langmuir isotherm and Freundlich isotherm show best-fitted observation in the case of linear and non-linear isotherm respectively (R2 = 0.96 & R2 = 0.957). In the case of kinetic linear models, the data was well fitted with pseudo-second-order showing chemosorption mechanism (R2 = 0.999), and in the case of non-linear kinetic model pseudo first order showed good fit showing physisorption mechanism during adsorption (R2 = 0.999). The thermodynamic study showed positive values for ΔH° and ΔS° throughout the adsorption process respectively, implying an endothermic behaviour. In view of cost effectiveness, desorption or regeneration study was done and it was showed that after the 5th cycle, the removal tendency had decreased from 48 to 38 % for 20-100 ppm dye solution accordingly. Thus, nanocomposite beads prepared by the coagulation method seem to be a suitable candidate for dye removal from synthetic wastewater and may have potential to be used in small scale textile industries for real wastewater treatment.

Rapid identification and quantitative analysis of malachite green in fish via SERS and 1D convolutional neural network.

Rapid and quantitative detection of malachite green (MG) in aquaculture products is very important for safety assurance in food supply. Here, we develop a point-of-care testing (POCT) platform that combines a flexible and transparent surface-enhanced Raman scattering (SERS) substrate with deep learning network for achieving rapid and quantitative detection of MG in fish. The flexible and transparent SERS substrate was prepared by depositing silver (Ag) film on the polydimethylsiloxane (PDMS) film using laser molecular beam epitaxy (LMBE) technique. The wrinkled Ag NPs@PDMS film exhibits high SERS activity, excellent reproducibility and good mechanical stability. Additionally, the fast in situ detection of MG residues onfishscales was achieved by using the wrinkled Ag NPs/PDMS film and a portable Raman spectrometer, with a minimum detectable concentration of 10-6 M. Subsequently, a one-dimensional convolutional neural network (1D CNN) model was constructed for rapid quantification of MG concentration. The results demonstrated that the 1D CNN quantitative analysis model possessed superior predictive performance, with a coefficient of determination (R2) of 0.9947 and a mean squared error (MSE) of 0.0104. The proposed POCT platform, integrating a transparent flexible SERS substrate, a portable Raman spectrometer and a 1D CNN model, provides an efficient strategy for rapid identification and quantitative analysis of MG in fish.

Carboxyl-functionalized covalent organic frameworks for the extraction of malachite green and crystal violet in environmental water samples prior to quantification by high-performance liquid chromatography.

In this study, monodisperse, uniform, and spherical covalent organic frameworks (COFs) were synthesized using 1,3,5-tris (4-aminophenyl) benzene and 1,3,5-tricarboxaldehyde benzene at room temperature. Post-modification of 6-aminocaproic acid on the COFs yielded carboxyl-modified COFs (COFs-COOH). The modification enhanced the hydrophilicity and adsorption efficiencies of COFs-COOH for malachite green (MG) and crystal violet (CV). A COFs-COOH-based dispersive solid-phase extraction coupled with high-performance liquid chromatography was developed for the analysis of MG and CV. The method showed a linear range from 10 to 1000 ng/mL with detection limits of 1.82 and 0.70 ng/mL for MG and CV detection, respectively. The recoveries of MG and CV from water samples collected from fish farms and markets ranged from 91.63% to 107.10% with relative standard deviations below 5%. Reproducibility tests demonstrated that the adsorption efficiencies of COFs-COOH were maintained at above 85.86% over 15 cycles. The study verified the potential of COFs-COOH as sorbents for the enrichment and separation of triphenylmethane dyes from complex samples.

Artificial neural network-based modeling of Malachite green adsorption onto baru fruit endocarp: insights into equilibrium, kinetic, and thermodynamic behavior.

In this study, artificial neural network (ANN) tools were employed to forecast the adsorption capacity of Malachite green (MG) by baru fruit endocarp waste (B@FE) under diverse conditions, including pH, adsorbent dosage, initial dye concentration, contact time, and temperature. Enhanced adsorption efficiency was notably observed under alkaline pH conditions (pH 10). Kinetic analysis indicated that the adsorption process closely followed a pseudo-second-order model, while equilibrium studies revealed the Langmuir isotherm as the most suitable model, estimating a maximum adsorption capacity of 57.85 mg g-1. Furthermore, the chemical adsorption of MG by B@FE was confirmed using the Dubinin-Radushkevich isotherm. Thermodynamic analysis suggested that the adsorption is spontaneous and endothermic. Various ANN architectures were explored, employing different activation functions such as identity, logistic, tanh, and exponential. Based on evaluation metrics like the coefficient of determination (R2) and root mean square error (RMSE), the optimal network configuration was identified as a 5-11-1 architecture, consisting of five input neurons, eleven hidden neurons, and one output neuron. Notably, the logistic activation function was applied in both the hidden and output layers for this configuration. This study highlights the efficacy of B@FE as an efficient adsorbent for MG removal from aqueous solutions and demonstrates the potential of ANN models in predicting adsorption behavior across varying environmental conditions, emphasizing their utility in this field.

The innovative aspect of this study lies in the utilization of a new and effective adsorbent for the removal of Malachite Green (MG), derived from the fruit endocarp of baru (Dipteryx alata Vog.). The baru fruit endocarp, typically discarded as solid waste during processing, was found to possess favorable characteristics for adsorption processes and provides an adsorption capacity that exceeds that of most other similar adsorbents. Additionally, integrating Artificial Neural Networks (ANNs) enables accurate modeling of the adsorption process, eliminating the need for extensive laboratory experiments. This contributes significantly to wastewater treatment research, enhancing effectiveness and sustainability in unwanted dye removal.

Influence of Hydrothermal Modification on Adsorptive Performance of Clay Minerals for Malachite Green.

Artificially modified adsorbing materials mainly aim to remedy the disadvantages of natural materials as much as possible. Using clay materials such as rectorite, sodium bentonite and metakaolinite (solid waste material) as base materials, hydrothermally modified and unmodified materials were compared. CM-HT and CM (adsorbing materials) were prepared and used to adsorb and purify wastewater containing malachite green (MG) dye, and the two materials were characterized through methods such as BET, FT-IR, SEM and XRD. Results: (1) The optimal conditions for hydrothermal modification of CM-HT were a temperature of 150 °C, a time of 2 h, and a liquid/solid ratio 1:20. (2) Hydrothermal modification greatly increased the adsorptive effect. The measured maximum adsorption capacity of CM-HT for MG reached 290.45 mg/g (56.92% higher than that of CM). The theoretical maximum capacity was 625.15 mg/g (186.15% higher than that of CM). (3) Because Al-OH and Si-O-Al groups were reserved in unmodified clay mineral adsorbing materials with good adsorbing activity, after hydrothermal modification, the crystal structure of the clay became loosened along the direction of the c axis, and the interlayer space increased to partially exchange interlayer metal cations connected to the bottom oxygen, giving CM-HT higher electronegativity and creating more crystal defects and chemically active adsorbing sites for high-performance adsorption. (4) Chemical adsorption was the primary way by which CM-HT adsorbed cationic dye, while physical adsorption caused by developed pore canal was secondary. The adsorption reaction occurred spontaneously.

References for Small Fluorescence Quantum Yields.

Three compounds with fluorescence quantum yields in the range of 10- 5 to 10- 4 and emission spectra covering the UV/Vis spectral range are suggested as new references for the determination of small fluorescence quantum yields. The compounds are thymidine (dT) in water, dibenzoylmethane (DBM) in ethanol, and malachite green chloride (MG) in water, representing the blue, green, and red regions of the spectrum, respectively. All compounds are easily handled, photostable, and commercially available. Furthermore, these compounds exhibit a mirror-image symmetry between their absorption and fluorescence spectra. This symmetry, along with closely aligned fluorescence excitation and absorption spectra, confirms that the observed emissions originate from the compounds themselves. The fluorescence quantum yields were determined via a relative approach as well as Strickler-Berg analysis in conjunction with time resolved fluorescence spectroscopy. Within the respective error margins, the two approaches yielded identical results.

Zinc oxide decorated plantain peel activated carbon for adsorption of cationic malachite green dye: Mechanistic, kinetics and thermodynamics modeling.

Reports have shown that malachite green (MG) dye causes various hormonal disruptions and health hazards, hence, its removal from water has become a top priority. In this work, zinc oxide decorated plantain peels activated carbon (ZnO@PPAC) was developed via a hydrothermal approach. Physicochemical characterization of the ZnO@PPAC nanocomposite with a 205.2 m2/g surface area, porosity of 614.68 and dominance of acidic sites from Boehm study established the potency of ZnO@PPAC. Spectroscopic characterization of ZnO@PPAC vis-a-viz thermal gravimetric analyses (TGA), Fourier Transform Infrared Spectroscopy (FTIR), Powdered X-ray Diffraction (PXRD), Scanning Electron Microscopy and High Resolution - Transmission Electron Microscopy (HR-TEM) depict the thermal stability via phase transition, functional group, crystallinity with interspatial spacing, morphology and spherical and nano-rod-like shape of the ZnO@PPAC heterostructure with electron mapping respectively. Adsorption of malachite green dye onto ZnO@PPAC nanocomposite was influenced by different operational parameters. Equilibrium data across the three temperatures (303, 313, and 323 K) were most favorably described by Freundlich indicating the ZnO@PPAC heterogeneous nature. 77.517 mg/g monolayer capacity of ZnO@PPAC was superior to other adsorbents compared. Pore-diffusion predominated in the mechanism and kinetic data best fit the pseudo-second-order. Thermodynamics studies showed the feasible, endothermic, and spontaneous nature of the sequestration. The ZnO@PPAC was therefore shown to be a sustainable and efficient material for MG dye uptake and hereby endorsed for the treatment of industrial effluent.

Prediction of adsorption performance of ZIF-67 for malachite green based on artificial neural network using L-BFGS algorithm.

Given the necessity and urgency in removing organic pollutants such as malachite green (MG) from the environment, it is vital to screen high-capacity adsorbents using artificial neural network (ANN) methods quickly and accurately. In this study, a series of ZIF-67 were synthesized, which adsorption properties for organic pollutants, especially MG, were systematically evaluated and determined as 241.720 mg g-1 (25 â„ƒ, 2 h). The adsorption process was more consistent with pseudo-second-order kinetics and Langmuir adsorption isotherm, which correlation coefficients were 0.995 and 0.997, respectively. The chemisorption mechanism was considered to be π-π stacking interaction between imidazole and aromatic ring. Then, a Python-based neural network model using the Limited-memory BFGS algorithm was constructed by collecting the crucial structural parameters of ZIF-67 and the experimental data of batch adsorption. The model, optimized extensively, outperformed similar Matlab-based ANN with a coefficient of determination of 0.9882 and mean square error of 0.0009 in predicting ZIF-67 adsorption of MG. Furthermore, the model demonstrated a good generalization ability in the predictive training of other organic pollutants. In brief, ANN was successfully separated from the Matlab platform, providing a robust framework for high-precision prediction of organic pollutants and guiding the synthesis of adsorbents.

Reliable detection of malachite green by self-assembled SERS substrates based on gold-silicon heterogeneous nano pineapple structures.

Morphology regulation of heterodimer nanoparticles and the use of their asymmetric features for further practical applications are crucial because of the rich optical properties and various combinations of heterodimers. This work used silicon to asymmetrically wrap half of a gold sphere and grew gold branches on the bare gold surface to form heterogeneous nano pineapples (NPPs) which can effectively improve Surface-enhanced Raman scattering (SERS) properties through chemical enhancement and lightning-rod effect respectively. The asymmetric structures of NPPs enabled them to self-assemble into the monolayer membrane with consistent branch orientation. The prepared substrate had high homogeneity and better SERS ability than disorganized substrates, and achieved reliable detection of malachite green (MG) in clams with a detection limit of 7.8 × 10-11 M. This work provided a guide to further revise the morphology of heterodimers and a new idea for the use of asymmetric dimers for practically photochemical and biomedical sensing.

[A fluorescence method based on malachite green/aptamer for detection of ochratoxin A in traditional Chinese medicines].

A label-free fluorescence method based on malachite green/aptamer was developed for the detection of ochratoxin A(OTA) in traditional Chinese medicines. Malachite green itself exhibits weak fluorescence. Upon interaction with the aptamer specific to OTA, the G-quadruplex structure of the aptamer provides a protective microenvironment for malachite green, which significantly enhances its fluorescence signal. After OTA is added, preferential binding occurs between the aptamer and OTA, and malachite green will be released from the aptamer, which weakens the fluorescence signal. According to this principle, this paper established a fluorescence method with the aptamer of OTA as the recognition element and malachite green as the fluorescent probe for the detection of OTA in traditional Chinese medicines. The key experimental factors such as the concentrations of metal ions, aptamer, and malachite green were optimized to improve the performance of the method. OTA was detected under the optimal experimental conditions, and the results showed that with the increase in OTA concentration, the fluorescence signal gradually weakened. Within the range of 20-1 000 nmol·L~(-1), the OTA concentration was linearly correlated with the fluorescence signal ratio ΔF/F(ΔF=F_0-F, where F_0 is the fluorescence signal of aptamer/malachite green, and F is the fluorescence signal of OTA/aptamer/malachite green), with R~2 of 0.995. The limit of detection of the established method was 7.1 nmol·L~(-1). Furthermore, three substances structurally similar to OTA and two mycotoxins that may coexist with OTA were selected for experiments, which aimed to examine the cross-reactivity and specificity of the established method. The cross-reactivity experiments demonstrated that the interferers did not significantly affect the fluorescence signal of the detection system. The specificity experiments revealed that when mycotoxins were mixed with OTA, the fluorescence signal generated by the mixture closely resembled that of OTA itself. The results indicated that even in the presence of interferents, the established method remained unaffected and demonstrated excellent specificity. Additionally, this method exhibited remarkable reproducibility and stability. In the case of simple centrifugation and dilution of traditional Chinese medicine samples(Puerariae Lobatae Radix, Sophorae Flavescentis Radix, and Periplocae Cortex), the OTA detection method was applicable, with recovery rates ranging from 91.5% to 121.3%. Notably, this approach does not need complex pretreatment of traditional Chinese medicines while offering simple operation, low detection costs, and short detection time. Furthermore, by incorporating aptamers into the quality evaluation of traditional Chinese medicines, this method expands the application scope of aptamers.

Highly-Selective fluorescent Fe3O4@PPy aptasensor.

Label-free nucleic acid fluorescent probes are gaining popularity due to their low cost and ease of application. However, the primary challenges associated with label-free fluorescent probes stem from their tendency to interact with other biomolecules, such as RNA, proteins, and enzymes, which results in low specificity. In this work, we have developed a simple detection platform that utilizes Fe3O4@PPy in combination with a label-free nucleic acid probe, 1,1,2,2-tetrakis[4-(2-bromo-ethoxy)phenyl]ethene (TTAPE) or Malachite Green (MG), for highly selective detection of metal ions, acetamiprid, and thrombin. Fe3O4@PPy not only adsorbs aptamers through electrostatic interactions, π-π bonding, and hydrogen bonding, but also quenches the fluorescence of the TTAPE/MG. Upon the addition of target compounds, the aptasensor separates from Fe3O4@PPy through magnetic separation. Moreover, by changing different aptamers, the aptasensor was applied to detect metal ions, acetamiprid, and thrombin, with the turned-on photoluminescence (PL) emission intensity recorded and showing linearity to the concentrations of targets. The robustness of method was demonstrated by applying it to real samples, which included vegetables (for detecting acetamiprid with LODs of 0.02 and 0.04 ng/L), serum samples (for detecting thrombin with LODs of 5.5 and 4.3 nM), and water samples (for detecting Pb2+ with an LOD of 0.17 nM). Therefore, due to its impressive selectivity and sensitivity, the Fe3O4@PPy aptasensor could be utilized as a universal detection platform for various clinical and environmental applications.