A Survey of Algorithmic Debugging

"© ACM, 2017. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Computing Surveys, {50, 4, 2017} https://dl.acm.org/doi/10.1145/3106740"[EN] Algorithmic debugging is a technique proposed in 1982 by E. Y. Shapiro in the context of logic programming. This survey shows how the initial ideas have been developed to become a widespread debugging schema ftting many diferent programming paradigms and with applications out of the program debugging feld. We describe the general framework and the main issues related to the implementations in diferent programming paradigms and discuss several proposed improvements and optimizations. We also review the main algorithmic debugger tools that have been implemented so far and compare their features. From this comparison, we elaborate a summary of desirable characteristics that should be considered when implementing future algorithmic debuggers.This work has been partially supported by the EU (FEDER) and the Spanish Ministerio de Economia y Competitividad under grant TIN2013-44742-C4-1-R, TIN2016-76843-C4-1-R, StrongSoft (TIN2012-39391-C04-04), and TRACES (TIN2015-67522-C3-3-R) by the Generalitat Valenciana under grant PROMETEO-II/2015/013 (SmartLogic) and by the Comunidad de Madrid project N-Greens Software-CM (S2013/ICE-2731).Caballero, R.; Riesco, A.; Silva, J. (2017). A Survey of Algorithmic Debugging. ACM Computing Surveys. 50(4):1-35. https://doi.org/10.1145/3106740S135504Abramson, D., Foster, I., Michalakes, J., & Sosič, R. (1996). Relative debugging. Communications of the ACM, 39(11), 69-77. doi:10.1145/240455.240475K. R. Apt H. A. Blair and A. Walker. 1988. Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming J. Minker (Ed.). Morgan Kaufmann Publishers Inc. San Francisco CA 89--148. 10.1016/B978-0-934613-40-8.50006-3 K. R. Apt H. A. Blair and A. Walker. 1988. Towards a theory of declarative knowledge. In Foundations of Deductive Databases and Logic Programming J. Minker (Ed.). Morgan Kaufmann Publishers Inc. San Francisco CA 89--148. 10.1016/B978-0-934613-40-8.50006-3Arora, T., Ramakrishnan, R., Roth, W. G., Seshadri, P., & Srivastava, D. (1993). Explaining program execution in deductive systems. Lecture Notes in Computer Science, 101-119. doi:10.1007/3-540-57530-8_7E. Av-Ron. 1984. Top-Down Diagnosis of Prolog Programs. Ph.D. Dissertation. Weizmann Institute. E. Av-Ron. 1984. Top-Down Diagnosis of Prolog Programs. Ph.D. Dissertation. Weizmann Institute.A. Beaulieu. 2005. Learning SQL. O’Reilly Farnham UK. A. Beaulieu. 2005. Learning SQL. O’Reilly Farnham UK.D. Binks. 1995. Declarative Debugging in Gödel. Ph.D. Dissertation. University of Bristol. D. Binks. 1995. Declarative Debugging in Gödel. Ph.D. Dissertation. University of Bristol.B. Braßel and H. Siegel. 2008. Debugging Lazy Functional Programs by Asking the Oracle. Springer-Verlag Berlin 183--200. DOI:http://dx.doi.org/10.1007/978-3-540-85373-2_11 10.1007/978-3-540-85373-2_11 B. Braßel and H. Siegel. 2008. Debugging Lazy Functional Programs by Asking the Oracle. Springer-Verlag Berlin 183--200. DOI:http://dx.doi.org/10.1007/978-3-540-85373-2_11 10.1007/978-3-540-85373-2_11Caballero, R. (2005). A declarative debugger of incorrect answers for constraint functional-logic programs. Proceedings of the 2005 ACM SIGPLAN workshop on Curry and functional logic programming - WCFLP ’05. doi:10.1145/1085099.1085102Caballero, R., García-Ruiz, Y., & Sáenz-Pérez, F. (2012). Declarative Debugging of Wrong and Missing Answers for SQL Views. Lecture Notes in Computer Science, 73-87. doi:10.1007/978-3-642-29822-6_9Caballero, R., García-Ruiz, Y., & Sáenz-Pérez, F. (2015). Debugging of wrong and missing answers for datalog programs with constraint handling rules. Proceedings of the 17th International Symposium on Principles and Practice of Declarative Programming - PPDP ’15. doi:10.1145/2790449.2790522Caballero, R., Martin-Martin, E., Riesco, A., & Tamarit, S. (2015). A zoom-declarative debugger for sequential Erlang programs. Science of Computer Programming, 110, 104-118. doi:10.1016/j.scico.2015.06.011Caballero, R., & Rodríguez-Artalejo, M. (2002). A Declarative Debugging System for Lazy Functional Logic Programs. Electronic Notes in Theoretical Computer Science, 64, 113-175. doi:10.1016/s1571-0661(04)80349-9Ceri, S., Gottlob, G., & Tanca, L. (1989). What you always wanted to know about Datalog (and never dared to ask). IEEE Transactions on Knowledge and Data Engineering, 1(1), 146-166. doi:10.1109/69.43410Chen, M., Mao, S., & Liu, Y. (2014). Big Data: A Survey. Mobile Networks and Applications, 19(2), 171-209. doi:10.1007/s11036-013-0489-0Chitil, O., & Davie, T. (2008). Comprehending finite maps for algorithmic debugging of higher-order functional programs. Proceedings of the 10th international ACM SIGPLAN symposium on Principles and practice of declarative programming - PPDP ’08. doi:10.1145/1389449.1389475Chitil, O., Faddegon, M., & Runciman, C. (2016). A Lightweight Hat. Proceedings of the 28th Symposium on the Implementation and Application of Functional Programming Languages - IFL 2016. doi:10.1145/3064899.3064904O. Chitil C. Runciman and M. Wallace. 2001. Freja Hat and Hood—A Comparative Evaluation of Three Systems for Tracing and Debugging Lazy Functional Programs. Springer Berlin 176--193. O. Chitil C. Runciman and M. Wallace. 2001. Freja Hat and Hood—A Comparative Evaluation of Three Systems for Tracing and Debugging Lazy Functional Programs. Springer Berlin 176--193.O. Chitil C. Runciman and Malcolm Wallace. 2003. Transforming Haskell for Tracing. Springer-Verlag Berlin 165--181. DOI:http://dx.doi.org/10.1007/3-540-44854-3_11 10.1007/3-540-44854-3_11 O. Chitil C. Runciman and Malcolm Wallace. 2003. Transforming Haskell for Tracing. Springer-Verlag Berlin 165--181. DOI:http://dx.doi.org/10.1007/3-540-44854-3_11 10.1007/3-540-44854-3_11Minh Ngoc Dinh, Abramson, D., & Chao Jin. (2014). Scalable Relative Debugging. IEEE Transactions on Parallel and Distributed Systems, 25(3), 740-749. doi:10.1109/tpds.2013.86Faddegon, M., & Chitil, O. (2015). Algorithmic debugging of real-world haskell programs: deriving dependencies from the cost centre stack. ACM SIGPLAN Notices, 50(6), 33-42. doi:10.1145/2813885.2737985Faddegon, M., & Chitil, O. (2016). Lightweight computation tree tracing for lazy functional languages. Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation - PLDI 2016. doi:10.1145/2908080.2908104Ferrand, G. (1987). Error diagnosis in logic programming an adaptation of E.Y. Shapiro’s method. The Journal of Logic Programming, 4(3), 177-198. doi:10.1016/0743-1066(87)90001-xFritzson, P., Shahmehri, N., Kamkar, M., & Gyimothy, T. (1992). Generalized algorithmic debugging and testing. ACM Letters on Programming Languages and Systems, 1(4), 303-322. doi:10.1145/161494.161498Fromherz, M. P. J. (s. f.). Towards declarative debugging of concurrent constraint programs. Lecture Notes in Computer Science, 88-100. doi:10.1007/bfb0019403Harman, M., & Hierons, R. (2001). An overview of program slicing. Software Focus, 2(3), 85-92. doi:10.1002/swf.41F. Henderson T. Conway Z. Somogyi D. Jeffery P. Schachte S. Taylor C. Speirs T. Dowd R. Becket M. Brown and P. Wang. 2014. The Mercury Language Reference Manual (Version 14.01.1). The University of Melbourne. F. Henderson T. Conway Z. Somogyi D. Jeffery P. Schachte S. Taylor C. Speirs T. Dowd R. Becket M. Brown and P. Wang. 2014. The Mercury Language Reference Manual (Version 14.01.1). The University of Melbourne.C. Hermanns and H. Kuchen. 2013. Hybrid Debugging of Java Programs. Springer-Verlag Berlin 91--107. DOI:http://dx.doi.org/10.1007/978-3-642-36177-7_6 10.1007/978-3-642-36177-7_6 C. Hermanns and H. Kuchen. 2013. Hybrid Debugging of Java Programs. Springer-Verlag Berlin 91--107. DOI:http://dx.doi.org/10.1007/978-3-642-36177-7_6 10.1007/978-3-642-36177-7_6Hirunkitti, V., & Hogger, C. J. (s. f.). A generalised query minimisation for program debugging. Lecture Notes in Computer Science, 153-170. doi:10.1007/bfb0019407Hughes, J. (2010). Software Testing with QuickCheck. Lecture Notes in Computer Science, 183-223. doi:10.1007/978-3-642-17685-2_6G. Hutton. 2016. Programming in Haskell. Cambridge University Press Cambridge UK. G. Hutton. 2016. Programming in Haskell. Cambridge University Press Cambridge UK.Insa, D., & Silva, J. (2010). An algorithmic debugger for Java. 2010 IEEE International Conference on Software Maintenance. doi:10.1109/icsm.2010.5609661Insa, D., & Silva, J. (2011). Optimal Divide and Query. Lecture Notes in Computer Science, 224-238. doi:10.1007/978-3-642-24769-9_17Insa, D., & Silva, J. (2011). An optimal strategy for algorithmic debugging. 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE 2011). doi:10.1109/ase.2011.6100055D. Insa and J. Silva. 2011c. Scaling Up Algorithmic Debugging with Virtual Execution Trees. Springer-Verlag Berlin 149--163. DOI:http://dx.doi.org/10.1007/978-3-642-20551-4_10 10.1007/978-3-642-20551-4_10 D. Insa and J. Silva. 2011c. Scaling Up Algorithmic Debugging with Virtual Execution Trees. Springer-Verlag Berlin 149--163. DOI:http://dx.doi.org/10.1007/978-3-642-20551-4_10 10.1007/978-3-642-20551-4_10D. Insa and J. Silva. 2015a. Automatic transformation of iterative loops into recursive methods. Information 8 Software Technology 58 (2015) 95--109. DOI:http://dx.doi.org/10.1016/j.infsof.2014.10.001 10.1016/j.infsof.2014.10.001 D. Insa and J. Silva. 2015a. Automatic transformation of iterative loops into recursive methods. Information 8 Software Technology 58 (2015) 95--109. DOI:http://dx.doi.org/10.1016/j.infsof.2014.10.001 10.1016/j.infsof.2014.10.001Insa, D., & Silva, J. (2015). A Generalized Model for Algorithmic Debugging. Lecture Notes in Computer Science, 261-276. doi:10.1007/978-3-319-27436-2_16Insa, D., Silva, J., & Riesco, A. (2013). Speeding Up Algorithmic Debugging Using Balanced Execution Trees. Lecture Notes in Computer Science, 133-151. doi:10.1007/978-3-642-38916-0_8Insa, D., Silva, J., & Tomás, C. (2013). Enhancing Declarative Debugging with Loop Expansion and Tree Compression. Lecture Notes in Computer Science, 71-88. doi:10.1007/978-3-642-38197-3_6K. Jensen and N. Wirth. 1974. PASCAL User Manual and Report. Springer-Verlag Berlin. 10.1007/978-3-662-21554-8 K. 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Department of Computer Science and Software Engineering The University of Melbourne.Naganuma, J., Ogura, T., & Hoshino, T. (s. f.). High-level design validation using algorithmic debugging. Proceedings of European Design and Test Conference EDAC-ETC-EUROASIC. doi:10.1109/edtc.1994.326833Naish, L. (1992). Declarative diagnosis of missing answers. New Generation Computing, 10(3), 255-285. doi:10.1007/bf03037939H. Nilsson. 1998. Declarative Debugging for Lazy Functional Languages. Ph.D. Dissertation. Linköping Sweden. H. Nilsson. 1998. Declarative Debugging for Lazy Functional Languages. Ph.D. Dissertation. Linköping Sweden.NILSSON, H. (2001). How to look busy while being as lazy as ever: the Implementation of a lazy functional debugger. Journal of Functional Programming, 11(6), 629-671. doi:10.1017/s095679680100418xNilsson, H., & Fritzson, P. (s. f.). Algorithmic debugging for lazy functional languages. 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The Journal of Logic Programming, 23(2), 125-149. doi:10.1016/0743-1066(94)00039-9Riesco, A., Verdejo, A., Martí-Oliet, N., & Caballero, R. (2012). Declarative debugging of rewriting logic specifications. The Journal of Logic and Algebraic Programming, 81(7-8), 851-897. doi:10.1016/j.jlap.2011.06.004DeRose, L., Gontarek, A., Vose, A., Moench, R., Abramson, D., Dinh, M. N., & Jin, C. (2015). Relative debugging for a highly parallel hybrid computer system. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC ’15. doi:10.1145/2807591.2807605Runeson, P. (2006). A survey of unit testing practices. IEEE Software, 23(4), 22-29. doi:10.1109/ms.2006.91Russo, F., & Sancassani, M. (1992). A declarative debugging environment for DATALOG. Lecture Notes in Computer Science, 433-441. doi:10.1007/3-540-55460-2_32E. Y. Shapiro. 1982a. Algorithmic Program Debugging. MIT Press Cambridge MA. E. Y. Shapiro. 1982a. Algorithmic Program Debugging. MIT Press Cambridge MA.Shapiro, E. Y. (1982). Algorithmic program diagnosis. Proceedings of the 9th ACM SIGPLAN-SIGACT symposium on Principles of programming languages - POPL ’82. doi:10.1145/582153.582185Shmueli, O., & Tsur, S. (1991). Logical diagnosis ofLDL programs. New Generation Computing, 9(3-4), 277-303. doi:10.1007/bf03037166Silva, J. (s. f.). A Comparative Study of Algorithmic Debugging Strategies. Lecture Notes in Computer Science, 143-159. doi:10.1007/978-3-540-71410-1_11Silva, J. (2011). A survey on algorithmic debugging strategies. Advances in Engineering Software, 42(11), 976-991. doi:10.1016/j.advengsoft.2011.05.024Silva, J., & Chitil, O. (2006). Combining algorithmic debugging and program slicing. Proceedings of the 8th ACM SIGPLAN symposium on Principles and practice of declarative programming - PPDP ’06. doi:10.1145/1140335.1140355J. A. Silva E. R. Faria R. C. Barros E. R. Hruschka A. C. P. L. F. de Carvalho and J. Gama. 2013. 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Speeding Up Algorithmic Debugging Using Balanced Execution Trees

Algorithmic debugging is a debugging technique that uses a data structure representing all computations performed during the execution of a program. This data structure is the so-called Execution Tree and it strongly influences the performance of the technique. In this work we present a transformation that automatically improves the structure of the execution trees by collapsing and projecting some strategic nodes. This improvement in the structure implies a better behavior and performance of the standard algorithms that traverse it. We prove that the transformation is sound in the sense that all the bugs found after the transformation are real bugs; and if at least one bug is detectable before the transformation, then at least one bug will also be detectable after the transformation. We have implemented the technique and performed several experiments with real applications. The experimental results confirm the usefulness of the technique.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovación under grants TIN2008-06622-C03-02 and TIN2012-39391-004-04, by the Generalitat Valenciana under grant ACOMP/2009/017, and by the Comunidad de Madrid under grant S2009/TIC-1465. David Insa has been partially supported by the Spanish Ministerio de Educación under grant AP2010-4415.Silva, J.; Insa Cabrera, D.; Riesco, A. (2013). Speeding Up Algorithmic Debugging Using Balanced Execution Trees. En Tests and Proofs. Springer. 133-151. https://doi.org/10.1007/978-3-642-38916-0_8S133151Binks, D.: Declarative Debugging in Gödel. PhD thesis, University of Bristol (1995)Caballero, R.: A Declarative Debugger of Incorrect Answers for Constraint Functional-Logic Programs. In: Proc. of the 2005 ACM SIGPLAN Workshop on Curry and Functional Logic Programming, WCFLP 2005, pp. 8–13. ACM Press (2005)Caballero, R., Hermanns, C., Kuchen, H.: Algorithmic debugging of Java programs. In: López-Fraguas, F.J. (ed.) Proc. of the 15th Workshop on Functional and (Constraint) Logic Programming, WFLP 2006, Madrid, Spain. ENTCS, vol. 177, pp. 75–89. Elsevier (2007)Calejo, M.: A Framework for Declarative Prolog Debugging. PhD thesis, New University of Lisbon (1992)Davie, T., Chitil, O.: Hat-delta: One Right Does Make a Wrong. In: Seventh Symposium on Trends in Functional Programming, TFP 2006 (April 2006)Hirunkitti, V., Hogger, C.J.: A Generalised Query Minimisation for Program Debugging. In: Fritzson, P.A. (ed.) AADEBUG 1993. LNCS, vol. 749, pp. 153–170. Springer, Heidelberg (1993)Insa, D., Silva, J.: Scaling up algorithmic debugging with virtual execution trees. In: Alpuente, M. (ed.) LOPSTR 2010. LNCS, vol. 6564, pp. 149–163. Springer, Heidelberg (2011)Insa, D., Silva, J., Riesco, A.: Speeding up algorithmic debugging using balanced execution trees—detailed results. Technical Report 04/13, Departamento de Sistemas Informáticos y Computación (April 2013)Kokai, G., Nilson, J., Niss, C.: GIDTS: A Graphical Programming Environment for Prolog. In: Workshop on Program Analysis For Software Tools and Engineering, PASTE 1999, pp. 95–104. ACM Press (1999)MacLarty, I.: Practical Declarative Debugging of Mercury Programs. PhD thesis, Department of Computer Science and Software Engineering, University of Melbourne (2005)Maeji, M., Kanamori, T.: Top-Down Zooming Diagnosis of Logic Programs. Technical Report TR-290, Japan (1987)Nilsson, H.: Declarative Debugging for Lazy Functional Languages. PhD thesis, Linköping, Sweden (May 1998)Nilsson, H., Fritzson, P.: Algorithmic Debugging for Lazy Functional Languages. Journal of Functional Programming 4(3), 337–370 (1994)Shapiro, E.Y.: Algorithmic Program Debugging. MIT Press (1982)Silva, J.: A Survey on Algorithmic Debugging Strategies. Advances in Engineering Software 42(11), 976–991 (2011

El algebrista: una plataforma para el desarrollo de sistema tutores con componente inteligente en el área del álgebra

Este trabajo resume los propósitos, los procedimientos y algunos de los hallazgos de una línea de investigación cuyo propósito se puede resumir en las siguientes preguntas: ¿es posible formalizar el conocimiento que utiliza un tutor al mediar el aprendizaje del algebra y usar esos formalismos para desarrollar sistemas computacionales que los emulen?, en esa búsqueda,¿ se puede utilizar la formalización del conocimiento mediante técnica de la Inteligencia Artificial para comprender mejor como actuamos al mediar el aprendizaje? Esto es, la investigaci6n tuvo tanto el propósito de desarrollar sistemas tutoriales con apoyo informático, como aprender acerca de formas efectivas para facilitar el aprendizaje. El trabajo describe la arquitectura de los sistemas, muestra que fue necesario crear un espacio —decimos plataforma de desarrollo— que permitiese representar el conocimiento utilizado por los tutores observados. El estudio mostro las serias limitaciones que la tecnología impone a la representación de Ia actuación humana a la vez que su potencial en el área estudiada. Los principales hallazgos se encuentran en la nueva comprensión el conocimiento que emplea un docente mientras orienta el aprendizaje de un aprendiz

Detección y reconocimiento de guiños basado en análisis EEG utilizando redes neuronales

This paper proposes an automatic eyewink interpretation system based on EEG signal analysis for human-machine interface to benefit people with disabilities. Our system investigates the use of the Emotiv EPOC as a relatively low cost new method for acquiring EEG signals and the implementation of Artificial Neural Networks (ANN) for the classification algorithm. The proposed algorithm has been found effective in detecting and classifying the eyewinks that then can be translated to valid command for human-machine interface. The performance of the proposed approach is investigated using two types of ANN topologies, and the results obtained indicate a high rate of classification accuracy

Magnetic carbon xerogels for the catalytic wet peroxide oxidation of 4-nitrophenol solutions

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Carbon nanotubes: a suitable material for catalytic wet peroxide oxidation of organic pollutants?

Carbon materials, such as activated carbons (AC), graphite and activated carbon xerogels, have been explored as metal-free catalysts for the catalytic wet peroxide oxidation (CWPO) of bio-refractory organic compounds, such as azo dyes and phenolic compounds [1-3]. At the same time, the application of carbon nanomaterials in catalysis, such as carbon nanotubes (CNT), has grown exponentially [4]. In the present work, commercial multiwalled carbon nanotubes (MWNT) were used in the CWPO of 2-nitrophenol and its activity compared to a commercial AC, used as received and after chemical modifications

Materiais de carbono para processos de oxidação catalítica com peróxido de hidrogénio

A crescente complexidade das águas residuais tem levado ao aumento do número de poluentes que são refractários aos processos convencionais de tratamento biológico. Tipicamente, a transferência deste tipo de poluentes para uma fase sólida – via adsorção – tem sido um método bastante utilizado; contudo, no sentido de promover a sua efetiva destruição, com especial incidência em águas residuais de origem industrial, surgem os processos avançados de oxidação (AOP, Advanced Oxidation Processes) – baseados na geração, e subsequente utilização, de radicais hidroxilo (HO•), conhecidos fortes agentes oxidantes. A oxidação catalítica com peróxido de hidrogénio (CWPO, Catalytic Wet Peroxide Oxidation) é um AOP que utiliza peróxido de hidrogénio (H2O2) e um catalisador para promover a produção de HO•, sendo eficiente na oxidação de uma gama alargada de poluentes em fase aquosa. A utilização de iões metálicos como catalisadores em processos de CWPO (e.g. processo de Fenton) é eficaz, contudo apresenta como principal desvantagem a presença de espécies metálicas na água residual tratada. No sentido de superar esta desvantagem, tem vindo a ser demonstrado, desde o final da década de 90, que materiais de carbono podem atuar eles próprios como catalisadores livres de metais em processos de CWPO; em trabalhos anteriores do nosso grupo foi estudada a atividade de carvões ativados, de xerogéis de carbono e de nanotubos de carbono para o processo de CWPO (Gomes et al., 2011; Ribeiro et al., 2012, Ribeiro et al., 2013)

From nano- to macro-scale: hybrid magnetic carbon nanocomposites as a tool for catalytic wet peroxide oxidation

There is a need for technological innovation in the water sector worldwide, as a result of the growing demand for water supplies – meeting increasingly restricted quality criteria, while coping with the increasing scarcity of clean water sources1. Under this context, the use of treated wastewater as an alternative water source has emerged as a topic of high priority2. However, meeting current quality requirements for wastewater reuse is a great challenge, in which nanotechnology holds a great potential1. Catalytic wet peroxide oxidation (CWPO) is a promising water/wastewater treatment technology; it enables the formation of highly oxidizing hydroxyl radicals (HO•) under atmospheric pressure and low to moderate temperatures, when a suitable catalyst is employed for the decomposition of hydrogen peroxide (H2O2)3. However, further improvement of catalyst design is still required in order to allow the scale-up of the CWPO technology towards real-scale applications. Bearing this in mind, our work has been focused on the development of highly active and stable hybrid magnetic carbon nanocomposites for CWPO. A detailed catalyst design at the nanoscale, based on the understanding of the surface reactions and interactions involved in the CWPO process, has recently allowed us to move forward towards the treatment of a real industrial wastewater with high pollutant load – collected from a mechanical biological treatment (MBT) plant for municipal solid waste processing. This communication reports the findings obtained in the last four years by our research group in this quest. A particular emphasis is given to the synergistic effects arising from the combination of iron-based catalysts with the easily tuned properties of carbon-based materials.This work was supported by: Project POCI-01-0145-FEDER-006984 – Associate Laboratory LSRE-LCM funded by FEDER through COMPETE2020 – Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT – Fundação para a Ciência e a Tecnologia. R.S. Ribeiro acknowledges the FCT individual Ph.D. grant SFRH/BD/94177/2013, with financing from FCT and the European Social Fund (through POPH and QREN).info:eu-repo/semantics/publishedVersio

Carbon nanotubes as base materials for water treatment processes

Chemical wastewater treatments are dependent on the addition of auxiliary oxidants, which may include molecular oxygen, ozone, and hydrogen peroxide, working on their own, or activated by means of a specialized catalyst or photocatalyst. Chemical treatments are by nature definitive processes, since they can lead to complete mineralization of the existing pollutants. However, this is seldom the case, when looking for a rational solution from the socio-economical point of view. In the case of industrial effluents, special treatments are often required, even when only a partial oxidative degradation is targeted, due to the complex nature of the pollutants (e.g. dyes, pharmaceuticals, oils, organics, inorganics and bio-compounds). Some compounds, like nitrophenols, are particularly refractory to aerobic biodegradation and in addition to that toxic, requiring strong oxidative solutions. Typical solutions are the thermal processes at elevated temperatures and pressures, or using metal supported catalysts. Alternatively, it is possible to use heterogeneous photocatalysis based on the efficient production of hydroxyl radicals. Somewhere between these two limits lies the catalytic wet peroxide oxidation (CWPO), an advanced oxidation process (AOP) involving the use of hydrogen peroxide (H2O2) as oxidation source and a suitable catalyst (typically iron based catalysts). The main role of the catalyst is to promote H2O2 decomposition through the formation of hydroxyl radicals (HO●) with high oxidizing potential, effective in the destruction of a huge range of pollutants [1,2]. This type of technology is especially attractive due to the use of mild conditions, simple equipment and the environmental safe oxidant H2O2.Work supported by project PTDC/AAC-AMB/110088/2009 and partially by project PEst–C/EQB/LA0020/2011, financed by FEDER through COMPETE - Programa Operacional Factores de Competitividade and by FCT - Fundação para a Ciência e a Tecnologia