Application of machine learning in the study of cobalt-based oxide catalysts for antibiotic degradation: An innovative reverse synthesis strategy.

This study addresses antibiotic pollution in global water bodies by integrating machine learning and optimization algorithms to develop a novel reverse synthesis strategy for inorganic catalysts. We meticulously analyzed data from 96 studies, ensuring quality through preprocessing steps. Employing the AdaBoost model, we achieved 90.57% accuracy in classification and an R²value of 0.93 in regression, showcasing strong predictive power. A key innovation is the Sparrow Search Algorithm (SSA), which optimizes catalyst selection and experimental setup tailored to specific antibiotics. Empirical experiments validated SSA's efficacy, with degradation rates of 94% for Levofloxacin and 97% for Norfloxacin, aligning closely with predictions within a 2% margin of error. This research advances theoretical understanding and offers practical applications in material science and environmental engineering, significantly enhancing catalyst design efficiency and accuracy through the fusion of advanced machine learning techniques and optimization algorithms.

High-performance, stable CoNi LDH@Ni foam composite membrane with innovative peroxymonosulfate activation for 2,4-dichlorophenol destruction.

In this study, the cobalt-nickel layered double hydroxides (CoNi LDH) were synthesized with a variety of Co/Ni mass ratio, as CoxNiy LDHs. In comparison, Co1Ni3 LDH presented the best peroxymonosulfate (PMS) activation efficiency for 2,4-dichlorophenol removal. Meanwhile, CoNi LDH@Nickel foam (CoNi LDH@NF) composite membrane was constructed for enhancing the stability of catalytic performance. Herein, CoNi LDH@NF-PMS system exerted high degradation efficiency of 99.22% within 90 min for 2,4-DCP when [PMS]0 = 0.4 g/L, Co1Ni3 LDH@NF = 2 cm × 2 cm (0.2 g/L), reaction temperature = 298 K. For the surface morphology and structure of the catalyst, it was demonstrated that the CoNi LDH@NF composite membrane possessed abundant cavity structure, good specific surface area and sufficient active sites. Importantly, ·OH, SO4·- and 1O2 played the primary role in the CoNi LDH@NF-PMS system for 2,4-DCP decomposition, which revealed the PMS activation mechanism in CoNi LDH@NF-PMS system. Hence, this study eliminated the stability and adaptability of CoNi LDH@NF composite membrane, proposing a new theoretical basis of PMS heterogeneous catalysts selection.

Green composite aerogel based on citrus peel/chitosan/bentonite for sustainable removal Cu(II) from water matrices.

Here, we propose a green and sustainable 3D porous aerogel based on citrus peel (CP), chitosan (CS), and bentonite (BT). This aerogel is prepared through a simple sol-gel and freeze-drying process and is designed for efficient capture of Cu(II) ions from water matrices. CCBA-2, with its abundance of active binding sites, exhibits an impressive Cu(II) adsorption yield of 861.58 mg/g. The adsorption isotherm and kinetics follow the Freundlich and pseudo-second-order models, respectively. In the presence of coexisting mixed-metal ions, CCBA-2 demonstrates a significantly higher selectivity coefficient (KdCu = 1138.5) for removing Cu(II) ions compared to other toxic metal ions. Furthermore, the adsorption of Cu(II) ions by CCBA-2 is not significantly affected by coexisting cations/anions, ionic strength, organic matter, or different water matrices. Dynamic fixed-bed column experiments show that the adsorption capacity of Cu(II) ions reaches 377.4 mg/g, and the Yoon-Nelson model accurately describes the adsorption process and breakthrough curve. Through experiments, FTIR, and XPS analyses, we propose a reasonable binding mechanism between CCBA-2 and metal cations, involving electrostatic attraction and chemical chelation between Cu(II) and the functional groups of the aerogel. CCBA-2 saturated with Cu(II) ions can be successfully regenerated by elution with 1 M HNO3, with only a slight decrease in adsorption efficiency (5.3%) after 5 adsorption-desorption cycles. Therefore, CCBA-2 offers a cost-effective and environmentally friendly material that can be considered as a viable alternative for the green and efficient removal of toxic Cu(II) ions from wastewater.

A novel iron molybdate photocatalyst with heterojunction-like band gap structure for organic pollutant degradation by activation of persulfate under simulated sunlight irradiation.

Advanced oxidation processes (AOPs) applied to wastewater treatment have become increasingly well developed and the ability of a single technology to remove difficult organic pollutants is limited. One of the main limiting factors is the insufficient variety and quantity of active species generated during the reaction process and catalyst failure. The coupling of the two methods is a practical and effective approach. In this study, different types of semiconductor persulfate (PS) activators, iron molybdate nanoparticles (I-FeMoO4, II-FeMoO4, and III-FeMoO4), were synthesized by simple solvothermal and calcination methods and applied to photo-assisted activation of PS systems. In addition, the relationship between the intrinsic physicochemical and optoelectronic properties of FeMoO4 and the catalytic degradation performance was revealed by a series of characterization tools, and the dominant catalysts were screened. At an unadjusted pH of 4.86, 0.6 g L-1 of PS and 0.4 g L-1 of I-FeMoO4 could achieve efficient degradation of several difficult organic dye contaminants (rhodamine b (Rh B), methylene blue (MB), malachite green (MG), methyl orange (MO), and tartrazine (TTZ)) and other antibiotic contaminants (sulfamethoxazole (SMX), tetracycline (TC), norfloxacin (NOR), and carbamazepine (CBZ)) within 5-60 min. Possible degradation mechanisms in the I-FeMoO4/PS/Light reaction system were suggested by radical trapping experiments and electron paramagnetic resonance (EPR) tests. Recovery tests demonstrated that I-FeMoO4 has good recoverable stability and did not cause secondary pollution. Finally, our study provided a new perspective on the application of coupled wastewater treatment technologies in the practical treatment of organic wastewater.

Reliable Optimization of Arbitrary Functions over Quantum Measurements.

As the connection between classical and quantum worlds, quantum measurements play a unique role in the era of quantum information processing. Given an arbitrary function of quantum measurements, how to obtain its optimal value is often considered as a basic yet important problem in various applications. Typical examples include but are not limited to optimizing the likelihood functions in quantum measurement tomography, searching the Bell parameters in Bell-test experiments, and calculating the capacities of quantum channels. In this work, we propose reliable algorithms for optimizing arbitrary functions over the space of quantum measurements by combining the so-called Gilbert's algorithm for convex optimization with certain gradient algorithms. With extensive applications, we demonstrate the efficacy of our algorithms with both convex and nonconvex functions.

Degradation of chlortetracycline hydrochloride by peroxymonosulfate activation on natural manganese sand through response surface methodology.

This work studies the degradation of chlortetracycline hydrochloride (CTC) by activated peroxymonosulfate (PMS) with natural manganese sand (NMS). Meanwhile, the NMS was characterized and analyzed by isothermal nitrogen adsorption (BET), energy-dispersive X-ray spectroscopy (EDS) and scanning electron microscope (SEM). It can be induced that NMS material may contain C, O, Al, Si, Fe, Mn, and K, and the proportion of each is 6%, 9%, 13%, 34%, 27%, 5%, and 6%. Critical parameters, including initial pH value, catalyst dosage, and PMS amount, were optimized through response surface methodology. One of the essential significances of response surface methodology (RSM) is the establishment and optimization of the mathematical model to reduce the complexity of the experimental process. It can provide the degree of mutual influence between various factors and optimize the response based on the investigated factors. Results indicated that 81.65% of CTC could be degraded under the optimized conditions of PMS amount 2.02 g/L, the NMS dosage 0.29 g/L and pH 3.87. Also, it shows that NMS is the most powerful of each factor on the degradation efficiency. We proposed the degradation pathways of CTC from the liquid chromatograph-mass spectrometer (LC-MS) results. Therefore, NMS could be applied as an efficient activator of peroxymonosulfate to purify the water and wastewater.

Environmental and energy implications of coal-based alternative vehicle fuel pathway from the life cycle perspective.

Coal-based fuels are effective alternative vehicle fuels in coal-rich countries, but whether it is beneficial to the environment and carbon neutrality has always been a concern. This study evaluates the energy efficiency, greenhouse gas (GHG) emissions, water footprint (WF), and environmental impact of coal-based diesel, coal-based synthetic natural gas, coal-based hydrogen, and coal-based electricity pathways from the life cycle perspective. Furthermore, scenario analysis predicts the impact of coal-rich countries on energy and emissions by adjusting vehicle fuel ratio. The results show that the coal-based diesel fuel pathway has lower energy efficiency and emits more GHGs. And GHGs are concentrated in fuel production stage. In terms of WF, the coal-based electricity pathway has the greatest benefits, which gray WF is dominant. From environmental impact perspective, a single vehicle fuel pathway cannot satisfy all impact categories. But the coal-based electricity pathway has the lowest value for the end-point environmental impact categories. Scenario analysis shows that the USA, India, and the European Union can significantly save energy and water resources and reduce GHG emissions with the increase in the proportion of alternative fuel vehicle in 2030. However, even a complete ban on the use of conventional gasoline vehicle in Norway will not reduce water consumption.

Efficient Experimental Verification of Quantum Gates with Local Operations.

Verifying the correct functioning of quantum gates is a crucial step toward reliable quantum information processing, but it becomes an overwhelming challenge as the system size grows due to the dimensionality curse. Recent theoretical breakthroughs show that it is possible to verify various important quantum gates with the optimal sample complexity of O(1/ε) using local operations only, where ε is the estimation precision. In this Letter, we propose a variant of quantum gate verification (QGV) that is robust to practical gate imperfections and experimentally realize efficient QGV on a 2-qubit controlled-not gate and a 3-qubit Toffoli gate using only local state preparations and measurements. The experimental results show that, by using only 1600 and 2600 measurements on average, we can verify with 95% confidence level that the implemented controlled-not gate and Toffoli gate have fidelities of at least 99% and 97%, respectively. Demonstrating the superior low sample complexity and experimental feasibility of QGV, our work promises a solution to the dimensionality curse in verifying large quantum devices in the quantum era.

Universally Optimal Verification of Entangled States with Nondemolition Measurements.

The efficient and reliable characterization of quantum states plays a vital role in most, if not all, quantum information processing tasks. In this work, we present a universally optimal protocol for verifying entangled states by employing the so-called quantum nondemolition measurements, such that the verification efficiency is equivalent to that of the optimal global strategy. Instead of being probabilistic as the standard verification strategies, our protocol is constructed sequentially, which is thus more favorable for experimental realizations. In addition, the target states are preserved in the protocol after each measurement, so can be reused in any subsequent tasks. We demonstrate the power of our protocol for the optimal verification of Bell states, arbitrary two-qubit pure states, and stabilizer states. We also prove that our protocol is able to perform tasks including fidelity estimation and state preparation.

Experimental Optimal Orienteering via Parallel and Antiparallel Spins.

Antiparallel spins are superior in orienteering to parallel spins. This intriguing phenomenon is tied to entanglement associated with quantum measurements rather than quantum states. Using photonic systems, we experimentally realize the optimal orienteering protocols based on parallel spins and antiparallel spins, respectively. The optimal entangling measurements for decoding the direction information from parallel spins and antiparallel spins are realized using photonic quantum walks, which is a useful idea that is of wide interest in quantum information processing and foundational studies. Our experiments clearly demonstrate the advantage of antiparallel spins over parallel spins in orienteering. In addition, entangling measurements can extract more information than local measurements even if no entanglement is present in the quantum states.

Quantifying Quantum Resources with Conic Programming.

Resource theories can be used to formalize the quantification and manipulation of resources in quantum information processing such as entanglement, asymmetry and coherence of quantum states, and incompatibility of quantum measurements. Given a certain state or measurement, one can ask whether there is a task in which it performs better than any resourceless state or measurement. Using conic programming, we prove that any general robustness measure (with respect to a convex set of free states or measurements) can be seen as a quantifier of such outperformance in some discrimination task. We apply the technique to various examples, e.g., joint measurability, positive operator valued measures simulable by projective measurements, and state assemblages preparable with a given Schmidt number.

Coherence Depletion in Quantum Algorithms.

Besides the superior efficiency compared to their classical counterparts, quantum algorithms known so far are basically task-dependent, and scarcely any common features are shared between them. In this work, however, we show that the depletion of quantum coherence turns out to be a common phenomenon in these algorithms. For all the quantum algorithms that we investigated, including Grover's algorithm, Deutsch-Jozsa algorithm, and Shor's algorithm, quantum coherence of the system states reduces to the minimum along with the successful execution of the respective processes. Notably, a similar conclusion cannot be drawn using other quantitative measures such as quantum entanglement. Thus, we expect that coherence depletion as a common feature can be useful for devising new quantum algorithms in the future.

Deterministic realization of collective measurements via photonic quantum walks.

Collective measurements on identically prepared quantum systems can extract more information than local measurements, thereby enhancing information-processing efficiency. Although this nonclassical phenomenon has been known for two decades, it has remained a challenging task to demonstrate the advantage of collective measurements in experiments. Here, we introduce a general recipe for performing deterministic collective measurements on two identically prepared qubits based on quantum walks. Using photonic quantum walks, we realize experimentally an optimized collective measurement with fidelity 0.9946 without post selection. As an application, we achieve the highest tomographic efficiency in qubit state tomography to date. Our work offers an effective recipe for beating the precision limit of local measurements in quantum state tomography and metrology. In addition, our study opens an avenue for harvesting the power of collective measurements in quantum information-processing and for exploring the intriguing physics behind this power.

Convex Optimization over Classes of Multiparticle Entanglement.

A well-known strategy to characterize multiparticle entanglement utilizes the notion of stochastic local operations and classical communication (SLOCC), but characterizing the resulting entanglement classes is difficult. Given a multiparticle quantum state, we first show that Gilbert's algorithm can be adapted to prove separability or membership in a certain entanglement class. We then present two algorithms for convex optimization over SLOCC classes. The first algorithm uses a simple gradient approach, while the other one employs the accelerated projected-gradient method. For demonstration, the algorithms are applied to the likelihood-ratio test using experimental data on bound entanglement of a noisy four-photon Smolin state [Phys. Rev. Lett. 105, 130501 (2010)PRLTAO0031-900710.1103/PhysRevLett.105.130501].