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Showing papers by "Indian Institute of Science published in 2018"


Journal ArticleDOI
TL;DR: In this paper, the authors have discussed the use and validity of ten important parameters, namely overpotential at a defined current density, iR-corrected over-potential, Tafel slope, exchange current density (j0), mass activity, specific activity, faradaic efficiency (FE), turnover frequency (TOF), electrochemically active surface area (ECSA), and measurement of double layer capacitance (Cdl) for different electrocatalytic materials that are frequently employed in both oxygen evolution reaction (OER) and HER.
Abstract: The number of research reports published in recent years on electrochemical water splitting for hydrogen generation is higher than for many other fields of energy research. In fact, electrochemical water splitting, which is conventionally known as water electrolysis, has the potential to meet primary energy requirements in the near future when coal and hydrocarbons are completely consumed. Due to the sudden and exponentially increasing attention on this field, many researchers across the world, including our group, have been exerting immense efforts to improve the electrocatalytic properties of the materials that catalyze the oxygen evolution reaction (OER) at the anode and the hydrogen evolution reaction (HER) at the cathode, aided by the recent revolutionary discovery of nanomaterials. However, the pressure on the researchers to publish their findings rapidly has caused them to make many unnoticed and unintentional errors, which is mainly due to lack of clear insight on the activity parameters. In this perspective, we have discussed the use and validity of ten important parameters, namely overpotential at a defined current density, iR-corrected overpotential at a defined current density, Tafel slope, exchange current density (j0), mass activity, specific activity, faradaic efficiency (FE), turnover frequency (TOF), electrochemically active surface area (ECSA) and measurement of double layer capacitance (Cdl) for different electrocatalytic materials that are frequently employed in both OER and HER. Experimental results have also been provided in support of our discussions wherever required. Using our critical assessments of the activity parameters of water splitting electrocatalysis, researchers can ensure precision and correctness when presenting their data regarding the activity of an electrocatalyst.

915 citations


Journal ArticleDOI
TL;DR: In this article, the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry was conducted.
Abstract: High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future

728 citations


Journal ArticleDOI
TL;DR: The synthesis of mesoporous silica nanoparticles and the factors influencing the size and morphology of this wonder carrier are discussed.
Abstract: Recent advancements in drug delivery technologies utilizing a variety of carriers have resulted in a path-breaking revolution in the approach towards diagnosis and therapy alike in the current times. Need for materials with high thermal, chemical and mechanical properties have led to the development of mesoporous silica nanoparticles (MSNs). These ordered porous materials have garnered immense attention as drug carriers owing to their distinctive features over the others. They can be synthesized using a relatively simple process, thus making it cost effective. Moreover, by controlling the parameters during the synthesis; the morphology, pore size and volume and particle size can be transformed accordingly. Over the last few years, a rapid increase in research on MSNs as drug carriers for the treatment of various diseases has been observed indicating its potential benefits in drug delivery. Their widespread application for the loading of small molecules as well as macromolecules such as proteins, siRNA and so forth, has made it a versatile carrier. In the recent times, researchers have sorted to several modifications in the framework of MSNs to explore its potential in drug resistant chemotherapy, antimicrobial therapy. In this review, we have discussed the synthesis of these multitalented nanoparticles and the factors influencing the size and morphology of this wonder carrier. The second part of this review emphasizes on the applications and the advances made in the MSNs to broaden the spectrum of its use especially in the field of biomedicine. We have also touched upon the lacunae in the thorough understanding of its interaction with a biological system which poses a major hurdle in the passage of this carrier to the clinical level. In the final part of this review, we have discussed some of the major patents filed in the field of MSNs for therapeutic purpose.

513 citations


Journal ArticleDOI
Albert M. Sirunyan, Armen Tumasyan, Wolfgang Adam1, Federico Ambrogi1  +2238 moreInstitutions (159)
TL;DR: In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented.
Abstract: Many measurements and searches for physics beyond the standard model at the LHC rely on the efficient identification of heavy-flavour jets, i.e. jets originating from bottom or charm quarks. In this paper, the discriminating variables and the algorithms used for heavy-flavour jet identification during the first years of operation of the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13 TeV, are presented. Heavy-flavour jet identification algorithms have been improved compared to those used previously at centre-of-mass energies of 7 and 8 TeV. For jets with transverse momenta in the range expected in simulated events, these new developments result in an efficiency of 68% for the correct identification of a b jet for a probability of 1% of misidentifying a light-flavour jet. The improvement in relative efficiency at this misidentification probability is about 15%, compared to previous CMS algorithms. In addition, for the first time algorithms have been developed to identify jets containing two b hadrons in Lorentz-boosted event topologies, as well as to tag c jets. The large data sample recorded in 2016 at a centre-of-mass energy of 13 TeV has also allowed the development of new methods to measure the efficiency and misidentification probability of heavy-flavour jet identification algorithms. The b jet identification efficiency is measured with a precision of a few per cent at moderate jet transverse momenta (between 30 and 300 GeV) and about 5% at the highest jet transverse momenta (between 500 and 1000 GeV).

454 citations



Journal ArticleDOI
25 Jan 2018-Oncogene
TL;DR: The crucial role of METTL3-mediated m6A modification in GSC (neurosphere) maintenance and dedifferentiation of glioma cells is reported andMETTL3 is uncovered as a potential molecular target in GBM therapy.
Abstract: Despite advances in biology and therapeutic modalities, existence of highly tumorigenic glioma stem-like cells (GSCs) makes glioblastomas (GBMs) invincible. N6-methyl adenosine (m6A), one of the abundant mRNA modifications catalyzed by methyltransferase-like 3 and 14 (METTL3/14), influences various events in RNA metabolism. Here, we report the crucial role of METTL3-mediated m6A modification in GSC (neurosphere) maintenance and dedifferentiation of glioma cells. METTL3 expression is elevated in GSC and attenuated during differentiation. RNA immunoprecipitation studies identified SOX2 as a bonafide m6A target of METTL3 and the m6A modification of SOX2 mRNA by METTL3 enhanced its stability. The exogenous overexpression of 3'UTR-less SOX2 significantly alleviated the inhibition of neurosphere formation observed in METTL3 silenced GSCs. METTL3 binding and m6A modification in vivo required intact three METTL3/m6A sites present in the SOX2-3'UTR. Further, we found that the recruitment of Human antigen R (HuR) to m6A-modified RNA is essential for SOX2 mRNA stabilization by METTL3. In addition, we found a preferential binding by HuR to the m6A-modified transcripts globally. METTL3 silenced GSCs showed enhanced sensitivity to γ-irradiation and reduced DNA repair as evidenced from the accumulation of γ-H2AX. Exogenous overexpression of 3'UTR-less SOX2 in METTL3 silenced GSCs showed efficient DNA repair and also resulted in the significant rescue of neurosphere formation from METTL3 silencing induced radiosensitivity. Silencing METTL3 inhibited RasV12 mediated transformation of mouse immortalized astrocytes. GBM tumors have elevated levels of METTL3 transcripts and silencing METTL3 in U87/TIC inhibited tumor growth in an intracranial orthotopic mouse model with prolonged mice survival. METTL3 transcript levels predicted poor survival in GBMs which are enriched for GSC-specific signature. Thus our study reports the importance of m6A modification in GSCs and uncovers METTL3 as a potential molecular target in GBM therapy.

423 citations


Journal ArticleDOI
TL;DR: The Never-Ending Language Learner (NELL) as discussed by the authors is a case study of a machine learning system that learns to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (e.g., servedWith(tea,biscuits), while learning thousands of interrelated functions that continually improve its reading competence over time.
Abstract: Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on statistical analysis of a single data set We suggest that people learn better than computers precisely because of this difference, and we suggest a key direction for machine learning research is to develop software architectures that enable intelligent agents to also learn many types of knowledge, continuously over many years, and to become better learners over time In this paper we define more precisely this never-ending learning paradigm for machine learning, and we present one case study: the Never-Ending Language Learner (NELL), which achieves a number of the desired properties of a never-ending learner NELL has been learning to read the Web 24hrs/day since January 2010, and so far has acquired a knowledge base with 120mn diverse, confidence-weighted beliefs (eg, servedWith(tea,biscuits)), while learning thousands of interrelated functions that continually improve its reading competence over time NELL has also learned to reason over its knowledge base to infer new beliefs it has not yet read from those it has, and NELL is inventing new relational predicates to extend the ontology it uses to represent beliefs We describe the design of NELL, experimental results illustrating its behavior, and discuss both its successes and shortcomings as a case study in never-ending learning NELL can be tracked online at http://rtwmlcmuedu, and followed on Twitter at @CMUNELL

397 citations


Journal ArticleDOI
TL;DR: The findings regarding the design, fabrication and photophysical properties of 2D/2D heterostructure systems may find use in other photocatalytic applications including H2 production and water purification.
Abstract: 2D/2D interface heterostructures of g-C3N4 and NiAl-LDH are synthesized utilizing strong electrostatic interactions between positively charged 2D NiAl-LDH sheets and negatively charged 2D g-C3N4 nanosheets. This new 2D/2D interface heterojunction showed remarkable performance for photocatalytic CO2 reduction to produce renewable fuels such as CO and H2 under visible-light irradiation, far superior to that of either single phase g-C3N4 or NiAl-LDH nanosheets. The enhancement of photocatalytic activity could be attributed mainly to the excellent interfacial contact at the heterojunction of g-C3N4/NiAl-LDH, which subsequently results in suppressed recombination, and improved transfer and separation of photogenerated charge carriers. In addition, the optimal g-C3N4/NiAl-LDH nanocomposite possessed high photostability after successive experimental runs with no obvious change in the production of CO from CO2 reduction. Our findings regarding the design, fabrication and photophysical properties of 2D/2D heterostructure systems may find use in other photocatalytic applications including H2 production and water purification.

396 citations


Journal ArticleDOI
TL;DR: This review underlines not only the strategies developed to suppress the coffee-ring effect but also sheds light on approaches to arrive at novel processes and materials.

376 citations



Journal ArticleDOI
TL;DR: Using first-principles density functional theory calculations, the emergence of ultraflatbands at the valence band edge in twisted bilayer MoS_{2}, a prototypical transition metal dichalcogenide, is shown.
Abstract: Ultraflatbands in twisted bilayers of two-dimensional materials have the potential to host strong correlations, including the Mott-insulating phase at half-filling of the band. Using first-principles density functional theory calculations, we show the emergence of ultraflatbands at the valence band edge in twisted bilayer ${\mathrm{MoS}}_{2}$, a prototypical transition metal dichalcogenide. The computed band widths, 5 and 23 meV for 56.5\ifmmode^\circ\else\textdegree\fi{} and 3.5\ifmmode^\circ\else\textdegree\fi{} twist angles, respectively, are comparable to that of twisted bilayer graphene near ``magic'' angles. Large structural transformations in the moir\'e patterns lead to formation of shear solitons at stacking boundaries and strongly influence the electronic structure. We extend our analysis for twisted bilayer ${\mathrm{MoS}}_{2}$ to show that flatbands can occur at the valence band edge of twisted bilayer ${\mathrm{WS}}_{2}$, ${\mathrm{MoSe}}_{2}$, and ${\mathrm{WSe}}_{2}$ as well.

Journal ArticleDOI
TL;DR: In this paper, the performance of the modified system is studied using proton-proton collision data at center-of-mass energy √s=13 TeV, collected at the LHC in 2015 and 2016.
Abstract: The CMS muon detector system, muon reconstruction software, and high-level trigger underwent significant changes in 2013–2014 in preparation for running at higher LHC collision energy and instantaneous luminosity. The performance of the modified system is studied using proton-proton collision data at center-of-mass energy √s=13 TeV, collected at the LHC in 2015 and 2016. The measured performance parameters, including spatial resolution, efficiency, and timing, are found to meet all design specifications and are well reproduced by simulation. Despite the more challenging running conditions, the modified muon system is found to perform as well as, and in many aspects better than, previously. We dedicate this paper to the memory of Prof. Alberto Benvenuti, whose work was fundamental for the CMS muon detector.

Journal ArticleDOI
TL;DR: This Account describes efforts at focusing down into mechanical properties of organic molecular crystals from the viewpoint of crystal engineering, which is the synthesis and design of functional molecular solids and presents examples where complex properties may be deliberately turned on or off in organic crystals.
Abstract: ConspectusMechanical properties of organic molecular crystals have been noted and studied over the years but the complexity of the subject and its relationship with diverse fields such as mechanochemistry, phase transformations, polymorphism, and chemical, mechanical, and materials engineering have slowed understanding. Any such understanding also needs conceptual advances—sophisticated instrumentation, computational modeling, and chemical insight—lack of such synergy has surely hindered progress in this important field. This Account describes our efforts at focusing down into this interesting subject from the viewpoint of crystal engineering, which is the synthesis and design of functional molecular solids. Mechanical properties of soft molecular crystals imply molecular movement within the solid; the type of property depends on the likelihood of such movement in relation to the applied stress, including the ability of molecules to restore themselves to their original positions when the stress is removed...

Journal ArticleDOI
TL;DR: In this paper, the authors define and calculate versions of complexity for free fermionic quantum field theories in 1 + 1 and 3 + 1 dimensions, adopting Nielsen's geodesic perspective in the space of circuits.
Abstract: We define and calculate versions of complexity for free fermionic quantum field theories in 1 + 1 and 3 + 1 dimensions, adopting Nielsen's geodesic perspective in the space of circuits. We do this both by discretizing and identifying appropriate classes of Bogoliubov-Valatin transformations, and also directly in the continuum by defining squeezing operators and their generalizations. As a closely related problem, we consider cMERA tensor networks for fermions: viewing them as paths in circuit space, we compute their path lengths. Certain ambiguities that arise in some of these results because of cutoff dependence are discussed.

Proceedings ArticleDOI
01 Jan 2018
TL;DR: HyTE is a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane and not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations.
Abstract: Knowledge Graph (KG) embedding has emerged as an active area of research resulting in the development of several KG embedding methods Relational facts in KG often show temporal dynamics, eg, the fact (Cristiano_Ronaldo, playsFor, Manchester_United) is valid only from 2003 to 2009 Most of the existing KG embedding methods ignore this temporal dimension while learning embeddings of the KG elements In this paper, we propose HyTE, a temporally aware KG embedding method which explicitly incorporates time in the entity-relation space by associating each timestamp with a corresponding hyperplane HyTE not only performs KG inference using temporal guidance, but also predicts temporal scopes for relational facts with missing time annotations Through extensive experimentation on temporal datasets extracted from real-world KGs, we demonstrate the effectiveness of our model over both traditional as well as temporal KG embedding methods

Journal ArticleDOI
TL;DR: A redox active and hydrogen bonded COF with ultrahigh stability with outstanding areal capacitance 1600 mF cm-2 (gravimetric 169 F g-1) and excellent cyclic stability without compromising its capacitive performance or Coulombic efficiency is reported.
Abstract: Covalent organic frameworks (COFs) have emerged as promising electrode materials in supercapacitors (SCs). However, their insoluble powder-like nature, poor capacitive performance in pristine form, integrated with inferior electrochemical stability is a primary concern for their long-term use in electrochemical devices. Keeping this in perspective, herein we report a redox active and hydrogen bonded COF with ultrahigh stability in conc. H2SO4 (18 M), conc. HCl (12 M) and NaOH (9 M). The as-synthesized COF fabricated as thin sheets were efficiently employed as a free-standing supercapacitor electrode material using 3 M aq. H2SO4 as an electrolyte. Moreover, the pristine COF sheet showcased outstanding areal capacitance 1600 mF cm–2 (gravimetric 169 F g–1) and excellent cyclic stability (>100 000) without compromising its capacitive performance or Coulombic efficiency. Moreover, as a proof-of-concept, a solid-state supercapacitor device was also assembled and subsequently tested.

Journal ArticleDOI
TL;DR: The present biosynthesis approach is rapid, inexpensive and eco-friendly and it yielded highly stable ZnO NPs with significant antioxidant and anticancer potential for the treatment of lung cancer and subsequent therapeutic applications.

Journal ArticleDOI
Valérie Turcot1, Yingchang Lu2, Yingchang Lu3, Heather M. Highland4  +486 moreInstitutions (129)
TL;DR: Exome-wide analysis identifies rare and low-frequency coding variants associated with body mass index that confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.
Abstract: Genome-wide association studies (GWAS) have identified >250 loci for body mass index (BMI), implicating pathways related to neuronal biology. Most GWAS loci represent clusters of common, noncoding variants from which pinpointing causal genes remains challenging. Here we combined data from 718,734 individuals to discover rare and low-frequency (minor allele frequency (MAF) < 5%) coding variants associated with BMI. We identified 14 coding variants in 13 genes, of which 8 variants were in genes (ZBTB7B, ACHE, RAPGEF3, RAB21, ZFHX3, ENTPD6, ZFR2 and ZNF169) newly implicated in human obesity, 2 variants were in genes (MC4R and KSR2) previously observed to be mutated in extreme obesity and 2 variants were in GIPR. The effect sizes of rare variants are ~10 times larger than those of common variants, with the largest effect observed in carriers of an MC4R mutation introducing a stop codon (p.Tyr35Ter, MAF = 0.01%), who weighed ~7 kg more than non-carriers. Pathway analyses based on the variants associated with BMI confirm enrichment of neuronal genes and provide new evidence for adipocyte and energy expenditure biology, widening the potential of genetically supported therapeutic targets in obesity.

Journal ArticleDOI
Albert M. Sirunyan1, Armen Tumasyan1, Wolfgang Adam, Federico Ambrogi  +2240 moreInstitutions (157)
TL;DR: In this article, a measurement of the H→ττ signal strength is performed using events recorded in proton-proton collisions by the CMS experiment at the LHC in 2016 at a center-of-mass energy of 13TeV.

Proceedings ArticleDOI
01 Jun 2018
TL;DR: In this paper, a growing CNN is proposed to progressively increase its capacity to account for the wide variability in the way people appear in crowd scenes, which is the major difficulty of crowd counting.
Abstract: Automated counting of people in crowd images is a challenging task. The major difficulty stems from the large diversity in the way people appear in crowds. In fact, features available for crowd discrimination largely depend on the crowd density to the extent that people are only seen as blobs in a highly dense scene. We tackle this problem with a growing CNN which can progressively increase its capacity to account for the wide variability seen in crowd scenes. Our model starts from a base CNN density regressor, which is trained in equivalence on all types of crowd images. In order to adapt with the huge diversity, we create two child regressors which are exact copies of the base CNN. A differential training procedure divides the dataset into two clusters and fine-tunes the child networks on their respective specialties. Consequently, without any hand-crafted criteria for forming specialties, the child regressors become experts on certain types of crowds. The child networks are again split recursively, creating two experts at every division. This hierarchical training leads to a CNN tree, where the child regressors are more fine experts than any of their parents. The leaf nodes are taken as the final experts and a classifier network is then trained to predict the correct specialty for a given test image patch. The proposed model achieves higher count accuracy on major crowd datasets. Further, we analyse the characteristics of specialties mined automatically by our method.

Journal ArticleDOI
TL;DR: These are the first direct limits for N mass above 500 GeV and the first limits obtained at a hadron collider for N masses below 40 Ge V.
Abstract: A search for a heavy neutral lepton N of Majorana nature decaying into a W boson and a charged lepton is performed using the CMS detector at the LHC. The targeted signature consists of three prompt charged leptons in any flavor combination of electrons and muons. The data were collected in proton-proton collisions at a center-of-mass energy of 13 TeV, with an integrated luminosity of 35.9 fb^(−1). The search is performed in the N mass range between 1 GeV and 1.2 TeV. The data are found to be consistent with the expected standard model background. Upper limits are set on the values of |V_(eN)|^2and |V_(μN)|^2, where V_(lN) is the matrix element describing the mixing of N with the standard model neutrino of flavor l. These are the first direct limits for N masses above 500 GeV and the first limits obtained at a hadron collider for N masses below 40 GeV.

Journal ArticleDOI
TL;DR: In this paper, an overview of recent progress in polyanionic framework compounds, with emphasis on high-voltage candidates consisting of earth abundant elements, is given, guided by ternary phase diagrams, recently discovered and potential cathode candidates are discussed gauging their performance, current status, and future perspectives.
Abstract: Efficient energy storage is a driving factor propelling myriads of mobile electronics, electric vehicles and stationary electric grid storage. Li-ion batteries have realized these goals in a commercially viable manner with ever increasing penetration to different technology sectors across the globe. While these electronic devices are more evident and appealing to consumers, there has been a growing concern for micro-to-mega grid storage systems. Overall, the modern world demands energy in terawatt' scale. It needs a multipronged approach with alternate technologies complementing the Li-ion batteries. One such viable approach is to design and implement Na-ion batteries. With the uniform geographical distribution, abundance and materials economy of Na resources as well as a striking operational similarity to Li-ion batteries, Na-ion batteries have commercial potential, particularly for applications unrestricted by volumetric/gravimetric energy density. In pursuit of the development of Na-ion batteries, suites of oxides, sulfides, fluorides, and polyanionic materials have been reported in addition to several organic complexes. This article gives an overview of recent progress in polyanionic framework compounds, with emphasis on high-voltage candidates consisting of earth abundant elements. Guided by ternary phase diagrams, recently discovered and potential cathode candidates will be discussed gauging their performance, current status, and future perspectives.


Journal ArticleDOI
TL;DR: The proposed manifesto addresses the major open challenges in Cloud computing by identifying themajor open challenges, emerging trends, and impact areas, and offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.
Abstract: The Cloud computing paradigm has revolutionised the computer science horizon during the past decade and has enabled the emergence of computing as the fifth utility. It has captured significant attention of academia, industries, and government bodies. Now, it has emerged as the backbone of modern economy by offering subscription-based services anytime, anywhere following a pay-as-you-go model. This has instigated (1) shorter establishment times for start-ups, (2) creation of scalable global enterprise applications, (3) better cost-to-value associativity for scientific and high-performance computing applications, and (4) different invocation/execution models for pervasive and ubiquitous applications. The recent technological developments and paradigms such as serverless computing, software-defined networking, Internet of Things, and processing at network edge are creating new opportunities for Cloud computing. However, they are also posing several new challenges and creating the need for new approaches and research strategies, as well as the re-evaluation of the models that were developed to address issues such as scalability, elasticity, reliability, security, sustainability, and application models. The proposed manifesto addresses them by identifying the major open challenges in Cloud computing, emerging trends, and impact areas. It then offers research directions for the next decade, thus helping in the realisation of Future Generation Cloud Computing.

Journal ArticleDOI
TL;DR: The present review critically analyses the variety of EF stimulation approaches that can be employed to evoke appropriate stem cell response and also makes an attempt to summarize the underlying concepts of this notion, placing special emphasis on stem cell based tissue engineering and regenerative medicine.


Journal ArticleDOI
TL;DR: In this paper, the authors study C2N/WS2 van der Waals heterostructure as a possible photocatalyst for water splitting and find that band edges of the heterostructures satisfy both water oxidation and reduction energy levels, ensuring the occurrence of these two reactions.

Journal ArticleDOI
TL;DR: The tensile properties, mode I fracture toughness, fatigue crack growth behavior, and unnotched fatigue strength of additively manufactured Ti-6Al-4V (Ti64) alloy using selective laser melting (SLM) technique were investigated in this article.

Journal ArticleDOI
TL;DR: In this article, an integrated solid waste management strategy is suggested to manage the organic fractions through technology and policy interventions, which helps in mitigating GHG emissions with potential economic benefits. But, this strategy necessitates understanding of composition of waste for its treatment and management in an environmentally sound way.
Abstract: Municipal solid waste in developing countries mainly consists of degradable materials (>70%), which plays a significant role in GHG (Greenhouse gas) emissions in urban localities. The increasing municipal solid waste generation along with the high fraction of organic waste and its unscientific disposal is leading to emission of GHG (methane, CO2, etc.) in the atmosphere. Proportion of municipal solid wastes collected by the agencies disposed at identified sites is about 60%, while the balance is disposed-off at unauthorized disposal sites leading to the environmental consequences including greenhouse gas emissions. Mitigation strategy necessitates understanding of composition of waste for its treatment and management in an environmentally sound way. The study revealed that the per capita waste generated is about 91.01± 45.5 g/day with the per capita organic waste generation of 74±35 g/person/day. The household per capita waste generation was positively related with income and education levels, while negatively related with family (household) size. The organic fractions constitute 82% with the strong recovery potential and conversion to energy or compost range. The total organic waste generated is about 231.01 Gg/year and due to mismanagement consequent emissions are about 604.80 Gg/year. Integrated solid waste management strategy is suggested to manage the organic fractions through technology and policy interventions, which helps in mitigating GHG emissions with potential economic benefits.

Journal ArticleDOI
TL;DR: In this paper, the authors used kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms to predict the band gap with the lowest root-mean-squared error of 0.14 eV.
Abstract: MXenes are two-dimensional (2D) transition metal carbides and nitrides, and are invariably metallic in pristine form. While spontaneous passivation of their reactive bare surfaces lends unprecedented functionalities, consequently a many-folds increase in number of possible functionalized MXene makes their characterization difficult. Here, we study the electronic properties of this vast class of materials by accurately estimating the band gaps using statistical learning. Using easily available properties of the MXene, namely, boiling and melting points, atomic radii, phases, bond lengths, etc., as input features, models were developed using kernel ridge (KRR), support vector (SVR), Gaussian process (GPR), and bootstrap aggregating regression algorithms. Among these, the GPR model predicts the band gap with lowest root-mean-squared error (rmse) of 0.14 eV, within seconds. Most importantly, these models do not involve the Perdew–Burke–Ernzerhof (PBE) band gap as a feature. Our results demonstrate that machin...